Vehicle collision alert system and method for facilitating vehicle collision avoidance

ABSTRACT

An impairment analysis (“IA”) computer system for detecting a driver or vehicle impairment to facilitate vehicle collision avoidance is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate a target vehicle via the plurality of sensors by scanning at least one of the target vehicle and a target driver of the target vehicle; (ii) receive sensor data including at least one of target driver data and target vehicle condition data; (iii) analyze the sensor data by determining at least one outlier within the sensor data; (iv) detect an impairment of at least one of the target driver and the target vehicle based upon the analysis; and/or (v) direct corrective action based upon the detected impairment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/615,191, filed Jan. 9, 2018, entitled “VEHICLECOLLISION ALERT SYSTEM AND METHOD,” and U.S. Provisional PatentApplication No. 62/631,191, filed Feb. 15, 2018, entitled “VEHICLECOLLISION ALERT SYSTEM AND METHOD,” the entire contents and disclosureof which are hereby incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present disclosure relates to implementing a vehicle collision alertsystem and, more particularly, to a network based system and method foralerting at least a first driver to impaired second drivers and/orimpaired vehicles to avoid collisions.

BACKGROUND

Vehicle collisions due to driving behavior such as impaired driving arewidespread. For example, many vehicle accidents occur due to driversbeing distracted (e.g., texting, talking on the phone), drowsy, and/orunder the influence of drugs or alcohol. Vehicle accidents caused byvehicle malfunction, such as steering and braking problems, are alsoprevalent. Poor driving behavior (e.g., distracted driving) and impropervehicle conditions place other drivers at risk every day. For example, asafe driver operating a brand new vehicle may be involved in an accidentdue to an impaired driver of another vehicle. Vehicle accidents may becostly, time consuming, and in serious cases, fatal.

Although many vehicles include safety features designed to preventcollisions, these systems may be generally based upon monitoring thevehicle operator's own driving behavior (e.g., lane departure warningand lane-keeping assist systems). Some safety features are used ordeployed when a vehicle accident occurs (e.g., airbags, inflatable seatbelts). Furthermore, vehicles possessing autonomous or semi-autonomoustechnology or functionality may reduce the risk of vehicle accidents dueto an operator's own driving behavior. However, these vehicles may besusceptible to causing accidents due to vehicle malfunction (e.g.,engine software problems). Therefore, there exists a need for a vehiclecollision alert system that may alert a driver to impaired driversand/or impaired vehicles in the driver's vicinity to facilitate takingpreventative measures to avoid vehicle collisions.

BRIEF SUMMARY

The present embodiments may relate to systems and methods fordetermining impaired drivers and/or impaired vehicles in real time, andgenerating an alert signal based upon captured data. The system mayinclude an impairment analysis (“IA”) computer system associated with ahost vehicle (e.g., first vehicle), a plurality of sensors on the hostvehicle, one or more user computer devices, a host vehicle controller, atarget vehicle (e.g., second vehicle) controller, and one or moreinsurance network computer devices. The IA computer system may beconfigured to: (i) interrogate a target vehicle by using a plurality ofsensors associated with a host vehicle including scanning the targetvehicle and a target driver; (ii) receive sensor data including targetdriver data and target vehicle condition data; (iii) analyze the sensordata by applying a baseline model to the sensor data; (iv) detect animpairment with the target driver or the target vehicle based upon theanalysis; and/or (v) output an alert signal to at least a host vehiclecontroller based upon detecting the impairment.

In one aspect, an impairment analysis (“IA”) computer system fordetecting a driver or vehicle impairment to facilitate vehicle collisionavoidance may be provided. The IA computer system may be associated witha host vehicle. The IA computer system may include a plurality ofsensors, and at least one processor in communication with the pluralityof sensors and at least one memory device. The at least one processormay be programmed to: (i) interrogate a target vehicle via the pluralityof sensors by scanning at least one of the target vehicle and a targetdriver of the target vehicle; (ii) receive sensor data including atleast one of target driver data and target vehicle condition data; (iii)analyze the sensor data by determining at least one outlier within thesensor data; (iv) detect an impairment of at least one of the targetdriver and the target vehicle based upon the analysis; and/or (v) directcorrective action based upon the impairment of the at least one of thetarget driver and the target vehicle. The computer system may includeadditional, less, or alternative functionality, including that discussedelsewhere herein.

In another aspect, a computer-implemented method for detecting animpairment may be provided. The method may be implemented using animpairment analysis (“IA”) computing device associated with a hostvehicle. The IA computing device may include at least one processor incommunication with at least one memory device. The method may include:(i) interrogating, by the IA computing device, a target vehicle by usinga plurality of sensors included on a host vehicle to scan at least oneof a target vehicle and a target driver; (ii) receiving, by the IAcomputing device, sensor data including at least one of target driverdata and target vehicle condition data; (iii) analyzing, by the IAcomputing device, by determining at least one outlier within the sensordata; (iv) detecting, by the IA computing device, an impairment of atleast one of the target driver and the target vehicle based upon theanalysis; and/or (v) directing, by the IA computing device, correctiveaction based upon the impairment of the at least one of the targetdriver and the target vehicle. The method may include additional, less,or alternative actions, including those discussed elsewhere herein.

In a further aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor detecting a driver and/or vehicle impairment to facilitate vehiclecollision avoidance may be provided. When executed by at least oneprocessor, the computer-executable instructions may cause the at leastone processor to: (1) interrogate a target vehicle via a plurality ofsensors by scanning at least one of the target vehicle and a targetdriver of the target vehicle; (2) receive sensor data including at leastone of target driver data and target vehicle condition data; (3) analyzethe sensor data by determining at least one outlier within the sensordata; (4) detect an impairment of at least one of the target driver andthe target vehicle based upon the analysis; and/or (5) direct correctiveaction based upon the impairment of the at least one of the targetdriver and the target vehicle. The storage media may include or directadditional, less, or alternate actions, including those discussedelsewhere herein.

In another aspect, an impairment analysis (“IA”) computer system fordetecting an impairment with a target vehicle or target driver may beprovided. The IA computer system may be associated with a host vehicle.The IA computer system may include a plurality of sensors. In someexemplary embodiments, the IA computer system may include an IAcomputing device that includes at least one processor in communicationwith at least one memory device. The at least one processor may beprogrammed to: (i) interrogate (or scan) a target vehicle by using aplurality of sensors associated with a host vehicle including scanningthe target vehicle and/or a target driver; (ii) receive sensor dataincluding target driver data and/or target vehicle condition data; (iii)analyze the sensor data by applying a baseline model to the sensor data;(iv) detect an impairment with the target driver and/or the targetvehicle based upon the analysis; and/or (v) output an alert signal to atleast a host vehicle controller, or directing taking other correctiveaction (such as in the case of an autonomous vehicle) based upondetecting that the target driver and/or target vehicle is impaired. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method for detecting animpairment with a target vehicle or target driver may be provided. Themethod may be implemented using an impairment analysis (“IA”) computingdevice associated with a host vehicle. The IA computing device mayinclude at least one processor in communication with at least one memorydevice. The method may include: (i) interrogating (or scan), by the IAcomputing device, a target vehicle by using a plurality of sensorsassociated with a host vehicle including scanning the target vehicleand/or a target driver; (ii) receiving, by the IA computing device,sensor data including target driver data and/or target vehicle conditiondata; (iii) analyzing, by the IA computing device, the sensor data byapplying a baseline model to the sensor data; (iv) detecting, by the IAcomputing device, an impairment with the target driver and/or the targetvehicle based upon the analysis; and/or (v) outputting, by the IAcomputing device, an alert signal to at least a host vehicle controller(or directing other corrective or vehicle collision preventive actions)based upon detecting that the target driver and/or target vehicle isimpaired. The method may include additional, less, or alternatefunctionality, including those discussed elsewhere herein.

In a further aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions may cause the at least one processorto: (i) interrogate a target vehicle by using a plurality of sensorsassociated with a host vehicle including scanning the target vehicleand/or a target driver; (ii) receive sensor data including target driverdata and/or target vehicle condition data; (iii) analyze the sensor databy applying a baseline model to the sensor data; (iv) detect animpairment with the target driver and/or the target vehicle based uponthe analysis; and/or (v) output an alert signal to at least a hostvehicle controller, or direct corrective or collision preventive driveror vehicle actions, based upon detecting that the target driver and/ortarget vehicle is impaired. The storage media may include additional,less, or alternate actions, including those discussed elsewhere herein.

In another aspect, an impairment analysis (“IA”) computer system foralerting a first driver of a first vehicle, or the first vehicle (suchas autonomous vehicle) to a driving hazard posed by a second vehicleoperated by a second driver may be provided. The IA computer system maybe associated with the first vehicle. In some exemplary embodiments, theIA computer system may include an IA computing device that includes atleast one processor in communication with at least one memory device.The at least one processor may be programmed to: (i) receive secondvehicle data including second driver data and/or second vehiclecondition data, wherein the second vehicle data is collected by aplurality of sensors associated with the first vehicle; (ii) analyze thesecond vehicle data by applying a baseline model to the second vehicledata; (iii) determine that the second vehicle poses a driving hazard tothe first vehicle based upon the analysis; and/or (iv) generate an alertsignal, or direct preventive actions (such as in the case that the firstvehicle is an autonomous vehicle), based upon the determination that thesecond driver or second vehicle poses a driving hazard to the firstvehicle. The computer system may include additional, less, oralternative functionality, including that discussed elsewhere herein.

In yet another aspect, a computer-implemented method for alerting afirst driver of a first vehicle, or the first vehicle (such as anautonomous vehicle) to a driving hazard posed by a second vehicleoperated by a second driver may be provided. The method may beimplemented using an impairment analysis (“IA”) computing deviceassociated with the first vehicle. The IA computing device may includeat least one processor in communication with at least one memory device.The method may include: (i) receiving, by the IA computing device,second vehicle data including second driver data and/or second vehiclecondition data, that is collected by a plurality of sensors associatedwith the first vehicle; (ii) analyzing, by the IA computing device, thesecond vehicle data by applying a baseline model to the second vehicledata; (iii) determining, by the IA computing device, that the secondvehicle poses a driving hazard to the first vehicle based upon theanalysis; and/or (iv) generating, by the IA computing device, an alertsignal, or directing corrective action (such as in the case that thefirst vehicle is an autonomous vehicle), based upon the determinationthat the second vehicle poses a driving hazard to the first vehicle. Themethod may include additional, less, or alternative actions, includingthose discussed elsewhere herein.

In another aspect, at least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon may beprovided. When executed by at least one processor, thecomputer-executable instructions may cause the at least one processorto: (i) receive second vehicle data including second driver data andsecond vehicle condition data, wherein the second vehicle data iscollected by a plurality of sensors associated with the first vehicle;(ii) analyze the second vehicle data by applying a baseline model to thesecond vehicle data; (iii) determine that the second vehicle poses adriving hazard to the first vehicle based upon the analysis; and/or (iv)generate an alert signal, or direct other corrective action, based uponthe determination that the second vehicle poses a driving hazard to thefirst vehicle. The storage media may include or direct additional, less,or alternate actions, including those discussed elsewhere herein.

Ina further aspect, an impairment analysis (“IA”) computer system fordetecting a driver or vehicle (including an autonomous orsemi-autonomous vehicle) impairment with a target vehicle or targetdriver may be provided. The IA computer system may be associated with ahost vehicle. The IA computer system may further include a plurality ofsensors. In some exemplary embodiments, the IA computer system mayinclude an IA computing device that includes at least one processor incommunication with at least one memory device. The at least oneprocessor may be programmed to: (i) interrogate a target vehicle,wherein the target vehicle operates in at least one of a semi-autonomouscontrol mode and/or an autonomous control mode, and wherein the targetvehicle is capable of wirelessly communicating with the host vehicle;(ii) receive, from the target vehicle, sensor data including targetdriver data and/or target vehicle condition data; (iii) analyze thesensor data by applying a baseline model to the sensor data; (iv) detectan impairment of the target driver and/or target vehicle based upon theanalysis; and/or (v) output an alert signal to a host vehiclecontroller, or direct other corrective vehicle action (such as in thecase of autonomous or semi-autonomous vehicle), based upon detectingthat the target driver or target vehicle is impaired. The computersystem may include additional, less, or alternative functionality,including that discussed elsewhere herein.

In another aspect, a computer-implemented method for detecting animpairment with a target vehicle or target driver may be provided. Themethod may be implemented using an impairment analysis (“IA”) computingdevice associated with a host vehicle. The IA computing device mayinclude at least one processor in communication with at least one memorydevice. The method may include: (i) interrogating, by the IA computingdevice, a target vehicle, wherein the target vehicle operates in atleast one of a semi-autonomous control mode and/or an autonomous controlmode, and wherein the target vehicle is capable of wirelesslycommunicating with the host vehicle; (ii) receiving, from the targetvehicle, sensor data including target driver data and/or target vehiclecondition data; (iii) analyzing, by the IA computing device, the sensordata by applying a baseline model to the sensor data; (iv) detecting, bythe IA computing device, an impairment of the target driver or targetvehicle based upon the analysis; and/or (v) outputting, by the IAcomputing device, an alert signal to a host vehicle controller, ordirecting other corrective or collision preventive vehicle action (suchas in the case of an autonomous or semi-autonomous vehicle), based upondetecting that the target driver and/or target vehicle is impaired. Themethod may include additional, less, or alternate actions, includingthose discussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions may cause the at least one processorto: (i) interrogate a target vehicle, wherein the target vehicleoperates in at least one of a semi-autonomous control mode and anautonomous control mode, and wherein the target vehicle is capable ofwirelessly communicating with a host vehicle; (ii) receive, from thetarget vehicle, sensor data including target driver data and/or targetvehicle condition data; (iii) analyze the sensor data by applying abaseline model to the sensor data; (iv) detect an impairment of thetarget driver and/or target vehicle based upon the analysis; and/or (v)output an alert signal to a host vehicle controller, or direct takingother vehicle collision preventive actions, based upon detecting thatthe target driver and/or target vehicle is impaired. The instructionsmay direct additional, less, or alternate functionality or actions,including those discussed elsewhere herein.

In a further aspect, a computer system for collecting real-time impaireddriving data may be provided. The computer system may be associated witha host vehicle. The computer system may further include a plurality ofsensors. The computer system may include at least one processor incommunication with the plurality of sensors and at least one memorydevice. The at least one processor may be programmed to: (i) interrogatea target vehicle via the plurality of sensors by scanning the targetvehicle and/or a target driver of the target vehicle; (ii) receivesensor data including target driver data and/or target vehicle conditiondata; (iii) analyze the sensor data by applying a baseline model to thesensor data; (iv) detect an impairment of the target driver and/ortarget vehicle based upon the analysis, wherein the impairment is one ofat least a high-risk impairment and a low-risk impairment; and/or (v)transmit the detected impairment to a remote-computing device to updatean insurance policy of an insurance holder. The computer system mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method for collectingreal-time impaired driving data may be provided. The method may beimplemented using a computing device associated with a host vehicle. Thecomputing device may include at least one processor in communicationwith at least one memory device. The method may include: (i)interrogating, by the computing device, a target vehicle by using aplurality of sensors included on a host vehicle to scan a target vehicleand/or a target driver; (ii) receiving, by the computing device, sensordata including target driver data and/or target vehicle condition data;(iii) analyzing, by the computing device, the sensor data by applying abaseline model to the sensor data; (iv) detecting, by the computingdevice, an impairment of the target driver and/or target vehicle basedupon the analysis, wherein the impairment is one of at least a high-riskimpairment and a low-risk impairment; and/or (v) transmitting, by thecomputing device, the detected impairment to a remote-computing deviceto update an insurance policy of an insurance holder. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions may cause the at least one processorto: (i) interrogate a target vehicle by using a plurality of sensorsincluded on a host vehicle to scan a target vehicle and/or a targetdriver; (ii) receive sensor data including target driver data and/ortarget vehicle condition data; (iii) analyze the sensor data by applyinga baseline model to the sensor data; (iv) detect an impairment of thetarget driver or target vehicle based upon the analysis, wherein theimpairment is one of at least a high-risk impairment and a low-riskimpairment; and/or (v) transmit the detected impairment to aremote-computing device to update an insurance policy of an insuranceholder. The instructions may direct additional, less, or alternateactions or functionality, including that discussed elsewhere herein.

In another aspect, an impairment analysis (“IA”) computer system fordetecting a driver or vehicle impairment may be provided. The IAcomputer system may be associated with a host vehicle. The IA computersystem may include a plurality of sensors. In some exemplaryembodiments, the IA computer system may include an IA computing devicethat includes at least one processor in communication with the pluralityof sensors and at least one memory device. The at least one processormay be programmed to: (i) interrogate a target vehicle via the pluralityof sensors by scanning at least one of the target vehicle and a targetdriver of the target vehicle; (ii) receive sensor data including atleast one of target driver data and target vehicle condition data; (iii)analyze the sensor data to determine whether at least one of lane driftand vehicle speed deviation for the target vehicle exceeds a respectivethreshold; (iv) detect an impairment of at least one of the targetdriver and the target vehicle based upon the analysis; and/or (v) directcollision avoidance action based upon the detection. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method for detecting animpairment may be provided. The method may be implemented using animpairment analysis (“IA”) computing device associated with a hostvehicle. The IA computing device may include at least one processor incommunication with at least one memory device. The method may include:(i) interrogating, by the IA computing device, a target vehicle by usinga plurality of sensors included on a host vehicle to scan at least oneof target vehicle and a target driver; (ii) receiving, by the IAcomputing device, sensor data including at least one of target driverdata and target vehicle condition data; (iii) analyzing, by the IAcomputing device, the sensor data to detect at least one of lanedeparture and speed deviation for the target vehicle; (iv) determining,by the IA computing device, that the at least one of lane departure andspeed deviation for the target vehicle is above a respectivepredetermined threshold; and/or (v) directing, by the IA computingdevice, collision avoidance action based upon the determination. Themethod may include additional, less, or alternative actions, includingthose discussed elsewhere herein.

In yet another aspect, an impairment analysis (“IA”) computer system foralerting a target driver of a target vehicle to a driving hazard posedby a host vehicle operated by a host driver may be provided. The IAcomputer system may be associated with the host vehicle. The IA computersystem may include at least one processor in communication with at leastone memory device, the at least one processor may be programmed to: (i)gather sensor data at the host vehicle, wherein the sensor data includesdata associated with the host vehicle and the host driver, and whereinthe sensor data is collected by a plurality of sensors included on thehost vehicle; (ii) analyze the sensor data by applying a baseline modelto the sensor data; (iii) determine, based upon the analysis, that thehost vehicle poses a risk to the target vehicle; and/or (iv) output analert message to the target vehicle, wherein the alert message includessensor data enabling the target vehicle to determine corrective action.The storage media may include or direct additional, less, or alternateactions, including those discussed elsewhere herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed systemsand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and are instrumentalitiesshown, wherein:

FIG. 1 illustrates a schematic diagram of an exemplary host vehiclehaving an impairment analysis (IA) computing device;

FIG. 2 illustrates a simplified block diagram of an exemplary systemincluding the exemplary host vehicle (shown in FIG. 1) and a targetvehicle, in accordance with the present disclosure;

FIG. 3 illustrates a flow chart of an exemplary process for detecting animpairment using the IA computing device shown in FIG. 1, in accordancewith the present disclosure;

FIG. 4 illustrates a simplified block diagram of an exemplary impairmentanalysis computer system for implementing the process shown in FIG. 3and FIG. 7, in accordance with the present disclosure;

FIG. 5 illustrates an exemplary configuration of a user computer device,in accordance with one embodiment of the present disclosure;

FIG. 6 illustrates an exemplary configuration of a server computerdevice, in accordance with one embodiment of the present disclosure;

FIG. 7A illustrates a flow chart of an exemplary process of determiningwhether a second vehicle, such as the target vehicle shown in FIG. 2,poses a driving hazard to a first vehicle, such as the host vehicleshown in FIG. 1, in accordance with one embodiment of the presentdisclosure;

FIG. 7B illustrates a flow chart of an exemplary process of determiningwhether a first or oncoming vehicle presents a collision risk to asecond vehicle;

FIG. 8 illustrates an exemplary embodiment of a sensor positioned on ahost vehicle (e.g., first vehicle) such as the host vehicle shown inFIG. 1, for detecting an impairment with a target vehicle and/or atarget driver, in accordance with one embodiment of the presentdisclosure;

FIG. 9 illustrates an exemplary use case of the exemplary sensor shownin FIG. 8 using the computer system shown in FIG. 4, in accordance withone embodiment of the present disclosure;

FIG. 10 illustrates another exemplary use case of the exemplary sensorshown in FIG. 8 using the computer system shown in FIG. 4, in accordancewith one embodiment of the present disclosure;

FIG. 11 illustrates a secondary view of the exemplary use case shown inFIG. 10, in accordance with one embodiment of the present disclosure;and

FIG. 12 illustrates a diagram of components of one or more exemplarycomputing devices that may be used in the computer system shown in FIG.4.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION OF THE DRAWINGS

The present embodiments may relate to, inter alia, systems and methodsfor capturing real time driving related data to generate alerts andprevent vehicle collisions. In one exemplary embodiment, the methods maybe performed by an impairment analysis (“IA”) computing device.

In the exemplary embodiment, the IA computing device may be associatedwith a host vehicle (e.g., first vehicle). The IA computing device maybe located in the host vehicle. In some embodiments, the IA computingdevice may be accessed remotely. The IA computing device may interrogatea target vehicle (e.g., second vehicle, such as an oncoming orapproaching vehicle) by using a plurality of sensors included on, orassociated with, the host vehicle for scanning the target vehicle and atarget driver (e.g., second driver).

The sensors may include, but are not limited to radar, LIDAR, GlobalPositioning System (GPS), video devices, imaging devices, cameras (e.g.,2D and 3D cameras), and audio recorders. The sensors may be positionedon the exterior and/or interior of the host vehicle. In someembodiments, some of the sensors may be integrated into the host vehicle(e.g., tire pressure sensors), or may be attached to the host vehicle.

In the exemplary embodiment, the host vehicle may be equipped with aplurality of cameras, or other image sensors. A front view camera may bepositioned near the front of the host vehicle to capture data ofoncoming traffic. Side view cameras may also be positioned near thedriver side and passenger side of the host vehicle to capture data ofparallel traffic. Additionally or alternatively, a rear view camera maybe positioned at the rear of the host vehicle to capture data of trafficapproaching from the back. In the exemplary embodiment, the plurality ofcameras (e.g., front view and side view cameras) may be used withdifferent types of sensors to acquire quality data necessary for the IAcomputing device to detect impairment. In the exemplary embodiment, thecameras capture continuous video data of oncoming and parallel trafficwhen the ignition of the host vehicle is on.

In the exemplary embodiment, the IA computing device may receive sensordata (e.g., second vehicle data) from a plurality of sensors included onthe host vehicle. The sensor data may include target driver data of atarget driver (e.g., second vehicle driver) and target vehicle conditiondata of the target vehicle (e.g., second vehicle). Target driver datamay include information associated with the target driver, and mayinclude positional data, such as head orientation, body posture, neckposition, and eye movement. Target driver data may also includeinformation as to driving behavior, such as vehicle lane maintenance(e.g., lane drifting), braking, and gap distance between the hostvehicle and the target vehicle.

Target vehicle condition data may include information associated withthe target vehicle, and may include information as to vehiclemaintenance (e.g., dashboard indicator lights/messages), enginecondition (e.g., abnormal engine noise), and road condition (e.g., tirepressure variation due to road conditions). In addition to target driverdata and target vehicle condition data, sensor data may generallyinclude information as to steering input, speed maintenance (e.g.,failure to maintain speed, speeding in excess of the posted speedlimit), acceleration, deceleration, lane position, and abnormalvariation in a dampening of a shock absorber. The conditions may besensed by the plurality of sensors. In other embodiments, the hostvehicle may acquire sensor data from the plurality of sensors on thehost vehicle, as well as from sensors on the target vehicle. In oneembodiment, lane drift or variation in speed may be detected by sensorsmounted on the host or target vehicles, and may indicate driverdrowsiness or distraction.

In some embodiments, the host vehicle and the target vehicle may haveautonomous or semi-autonomous vehicle-related functionalities thatenable vehicle-to-vehicle (V2V) wireless communication. In theseembodiments, the IA computing device of the host vehicle may receivetarget vehicle condition data and/or target driver data directly fromthe target vehicle, such as via one or more radio frequency links. Forexample, a target vehicle controller of the target vehicle may detectthat its operator (e.g., target driver) is impaired. The target vehiclecontroller may broadcast an alert signal to the V2V communicationnetwork, which may be received by the host vehicle. In this embodiment,the alert signal broadcast from the target vehicle may be received at awireless communications device of the host vehicle.

In the exemplary embodiment, the IA computing device may analyze thesensor data by applying a baseline model to the sensor data. Thebaseline model may include baseline conditions that represent safedriving conditions and standard vehicle maintenance conditions. In theexemplary embodiment, the baseline model (and its conditions) may beused to detect an impairment. “Impairment” may be used herein todescribe both an operator impairment of the target driver (e.g., seconddriver) and a vehicle impairment of the target vehicle (e.g., secondvehicle, which may include an autonomous or semi-autonomous vehicle).

An operator impairment may include driving behavior that deviates fromsafe driving practices such as, but not limited to, texting, talking toa passenger, talking on the phone, looking down at a phone, adjustingvehicle settings (e.g., mirror, radio station, vehicle temperature,display clock), eating, drinking, reading billboards, looking away fromoncoming traffic, holding items, reaching for items, having one hand onthe steering wheel, driving while drowsy, and driving while under theinfluence of drugs, alcohol, and/or medication causing drowsiness. Avehicle impairment of the target vehicle may include vehicle conditionsthat deviate from standard vehicle maintenance and place the targetvehicle at risk of malfunctioning. Examples of vehicle impairment mayrange from mechanical problems (e.g., worn brake pads, brake rotors,engine overheating, tire alignment) to basic vehicle maintenancefailure, such as replacing brake pads and/or rotating tires. Otherexamples of vehicle impairment may include faulty operation ofsemi-autonomous or autonomous features or systems. For instance,electronic components, processors, or sensors may degrade or becomeinoperable. Also, software versions directing such technology may becomedegraded, corrupted, obsolete, in need of upgrade, or hacked.

The IA computing device may compare the sensor data to the baselinemodel and determine whether the sensor data exceeds a first threshold.The IA computing device may categorize the sensor data as low impairmentif the sensor data does not exceed the first threshold. For example, theIA computing device may receive sensor data indicating that the targetvehicle's front tires are low in pressure. However, the IA computingdevice may determine that the tire pressures are not within a designatedrange of potentially causing a collision or other driving hazard. Inthese embodiments, the IA computing device may have multiple thresholds,and categorize the received sensor data as low, medium, or highimpairment.

The IA computing device may output an alert signal to a host vehiclecontroller based upon detecting that the target driver and/or targetvehicle is impaired. As mentioned above, the IA computing device may beconfigured to output alert signals for certain categories of impairment(e.g., medium and high). Using the example above, the IA computingdevice may not output an alert signal based upon the determination thatthe tire pressures are categorized as low impairment. In someembodiments, the host driver may be able to adjust the alert signalsettings before driving the host vehicle. In some embodiments, the IAcomputing device may output an alert signal to a target vehiclecontroller. In certain embodiments, the IA computing device may alsooutput an alert signal to a vehicle controller of a surrounding vehicle.The IA computing device may output an alert signal by transmittinginstructions to an auditory signal generator, a visual signal generator,and/or a haptic signal generator to output a warning alert (e.g.,auditory alert, visual alert, and/or haptic alert).

In some embodiments, the IA computing device may include the hostvehicle controller. In certain embodiments, the IA computing device andthe host vehicle controller may be the same. In some embodiments, thealert signal may escalate in frequency as the possibility of a collisionincreases. For example, red lights may flash on the instrument panel towarn of a potential collision. In this example, if host driver takes noaction as the target vehicle approaches, the steering wheel and driver'sseat of the host vehicle may subsequently vibrate, and continuousbeeping noises may emit from the audio system. In some embodiments, thehost driver may be able to select the type of alert signals the hostdriver wants, and adjust the settings accordingly before operating thehost vehicle.

In further embodiments, the IA computing device may store the sensordata in a memory device, and transmit the sensor data to aremote-computing device to update at least one of an underwriting modeland an actuarial model. In these embodiments, the sensor data may beused to adjust an insurance policy of an insurance policy holder (e.g.,target driver). In certain embodiments, the sensor data may be used todetermine the statistics surrounding impaired drivers and/or impairedvehicles based upon factors such as location (e.g., city, suburb, ruralarea, urban area), time of day (e.g., morning, midnight), day of week(e.g., weekend, holiday), vehicle types (e.g., sports vehicles, trucks,minivans), and traffic (e.g., rush hour). Modeling data may beextrapolated from the sensor data to evaluate risk associated withimpaired drivers and/or impaired vehicles.

Exemplary technical effects of the systems, methods, andcomputer-readable media described herein may include, for example: (i)providing a real-time alert system that warns a host driver (e.g., firstdriver) about impaired vehicles and/or impaired drivers of vehicles inthe host driver's vicinity; (ii) providing a host driver with an alertand/or multiple alerts that enable the host driver to take preventativeaction while driving; (iii) transmitting an alert to surroundingvehicles to warn the surrounding vehicles as to a potential collisionwherein the surrounding vehicles include a target vehicle (e.g., secondvehicle) and other vehicles near the host vehicle and/or the targetvehicle; (iv) improving vehicle alert systems by transmitting an alertsignal directly to an impaired driver; (v) accurately monitoring thedriving behavior and actions of motorists on the road; (vi) improvingreal-time data collection of motorist driving behavior by capturingcontinuous data of oncoming and parallel traffic during anytime of theday; (vii) improving mass data acquisition of real-time distracteddriving data; (viii) improving the accuracy of insurance models (e.g.,underwriting and actuarial models) used to make insurance decisions;and/or (ix) continuously improving the accuracy of data used to makeinsurance decisions.

Exemplary Host Vehicle

FIG. 1 depicts a view of an exemplary host vehicle (e.g., first vehicle)100. In some embodiments, host vehicle 100 may be an autonomous orsemi-autonomous vehicle capable of fulfilling the transportationcapabilities of a traditional automobile or other vehicle. In theseembodiments, host vehicle 100 may be capable of sensing its environmentand navigating without human input. In other embodiments, host vehicle100 may be a manual vehicle, such as a traditional automobile that iscontrolled by a human driver, such as a host driver 106.

Host vehicle 100 may include a plurality of sensors 102, an IA computingdevice 104, and a host vehicle controller 108. Sensors 102 may include,but are not limited to, radar, LIDAR, Global Positioning System (GPS),video devices, imaging devices, cameras (e.g., 2D and 3D cameras), andaudio recorders. The plurality of sensors 102 may detect the currentsurroundings and location of host vehicle 100. Specifically, sensors 102may be configured to detect a target vehicle (e.g., second vehicle) 204(shown in FIG. 2). Target vehicle 204 may be a surrounding vehicle ofoncoming and/or parallel traffic.

Sensors 102 may also be configured to detect a target driver (e.g.,second driver) (not shown) of target vehicle 204. Conditions of targetvehicle 204 detected by the plurality of sensors 102 may include speed,acceleration, gear, braking, cornering, vehicle operation, and otherconditions related to the operation of target vehicle, for example: atleast one of a measurement of at least one of speed, direction, rate ofacceleration, rate of deceleration, location, position, orientation, androtation of the vehicle, and a measurement of one or more changes to atleast one of speed, direction rate of acceleration, rate ofdeceleration, location, position, orientation, and rotation of thevehicle.

Plurality of sensors 102 may detect the presence of a target driver(e.g., second driver) (not shown) of target vehicle 204. In someembodiments, sensors 102 may detect one or more passengers (not shown)of target vehicle 204. In these embodiments, plurality of sensors 102may detect the presence of fastened seatbelts, the weight in each seatin the second vehicle, heat signatures, or any other method of detectinginformation about the target driver and passengers in target vehicle204.

In certain embodiments, sensors 102 may include occupant positionsensors to determine a location and/or position of the target driverand, in some embodiments, passengers in target vehicle. The location ofan occupant may identify a particular seat or other location within thetarget vehicle where the occupant is located. The position of theoccupant may include the occupant's body orientation, the location ofspecific limbs, and/or other positional data. In one example, sensors102 may include front view and/or side view cameras, LIDAR, radar,weight sensors, accelerometer, gyroscope, compass and/or other types ofsensors to identify the location and/or position of occupants withintarget vehicle 204.

In the exemplary embodiment, an impairment analysis (“IA”) computingdevice 104 may be configured to receive sensor data (e.g., secondvehicle data) from sensors 102. IA computing device 104 may interpretthe sensor data to determine whether target vehicle 204 and/or thetarget driver are impaired. In some embodiments, IA computing device 104may interpret the sensor data to determine whether target vehicle 204poses a driving hazard to host vehicle 100. In one example, IA computingdevice 104 may use computer vision methods to detect impairment due tooperator impairment of the target driver and/or due to vehicleimpairment of target vehicle 204.

IA computing device 104 may interpret the sensor data to determine ifthe target driver is impaired by analyzing positional data received fromsensors 102. Positional data may include a position of the targetdriver, a gaze direction of the target driver, a direction of facing ofthe target driver, a size of the target driver, and/or a skeletalpositioning of the target driver. The directional facing of the targetdriver may include whether the target driver is facing forward, reachingforward, reaching to the side, and/or has his/her head turned. The sizeof the target driver may include the vehicle user's height. The skeletalpositioning may include positioning of the target driver's joints,spine, arms, legs, torso, neck face, head, major bones, hands, and/orfeet. In some embodiments, positional data may also include a positionof a passenger occupying the passenger seat.

Where host vehicle 100 and target vehicle 204 are either semi-autonomousor autonomous vehicles, IA computing device 104 may receive sensor data(e.g., wirelessly communicated) from the target vehicle 204. In theseembodiments, IA computing device 104 may interpret the sensor datareceived from target vehicle 204 to determine if the target vehicle isimpaired. Sensor data received from target vehicle 204 may includevehicle telematics data collected by one or more sensors mounted on orinstalled within target vehicle 204, such as vehicle speed,acceleration, cornering, heading, direction, deceleration, braking, etc.The telematics data may also include a tread depth of one or morevehicle tires, an environmental sensor reading (e.g., temperature,humidity, acceleration), vehicle mileage, vehicle oil and fluid levels,tire pressure, tire temperature, vehicle brake pad thicknesses,gyroscope and accelerometer sensor information, GPS information, and thelike.

IA computing device 104 may collect and/or generate telematics dataassociated with driving characteristics of the target driver. Forexample, IA computing device 104 may collect telematics data of thetarget vehicle 204 and/or the target driver from one or more of sensors102 on host vehicle 100. In some embodiments, IA computing device 104may also receive telematics data of target vehicle 204 and/or the targetdriver operating target vehicle 204. For example, target vehicletelematics data collected and analyzed by IA computing device 104 mayinclude, but is not limited to positional data of the target driver,braking and/or acceleration data, navigation data, vehicle settings(e.g., seat position, mirror position, temperature or air controlsettings, etc.), and/or any other telematics data associated with targetvehicle 204 and/or the target driver.

In the exemplary embodiment, host vehicle controller 108 may beconfigured to generate an alert signal based upon the determination byIA computing device 104 that the target vehicle 204 and/or the targetdriver are impaired (e.g., pose a driving hazard to host vehicle 100).Host vehicle controller 108 may generate an auditory signal, a visualsignal, and/or a haptic signal to alert host driver (e.g., first driver)106 of an impaired driver and/or an impaired vehicle. For example, if IAcomputing device 104 determines that the target vehicle 204 and/or thetarget driver pose a driving hazard, the steering wheel of host vehicle100 may vibrate and an alert noise (e.g., beep/chime sound effect) mayemanate from the audio system of host vehicle 100.

In some embodiments, host vehicle controller 108 may include a displayscreen or touchscreen (not shown) that is capable of displaying an alertto host driver 106. In other embodiments, host vehicle controller 108may be capable of wirelessly communicating with a user computer device406 (shown in FIG. 4) such as a mobile device (not shown) in hostvehicle 100. In these embodiments, host vehicle controller 108 may becapable of communicating with the user of a mobile device, such as hostdriver 106, through an application on the mobile device. Moreover, wherehost vehicle 100 and the second vehicle are either autonomous orsemi-autonomous vehicles, host vehicle controller 108 may generate andtransmit the alert signal to target vehicle 204 to alert the targetdriver.

In some embodiments, IA computing device 104 may include host vehiclecontroller 108. In these embodiments, IA computing device 104 may beconfigured to generate an alert signal (e.g., auditory signal, visualsignal, and/or haptic signal) based upon a determination that targetvehicle 204 and/or the target driver is impaired. Further, where hostvehicle 100 and target vehicle 204 are either autonomous orsemi-autonomous vehicles, IA computing device 104 may generate andtransmit the alert signal to target vehicle 204 to alert the targetdriver.

In some embodiments, host vehicle 100 may include autonomous orsemi-autonomous vehicle-related functionality or technology that may beused with the present embodiments to replace human driver actions andmay include and/or be related to the following types of functionality:(a) fully autonomous (driverless); (b) limited driver control; (c)vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality. In these embodiments, the autonomous or semi-autonomousvehicle-related functionality or technology may be controlled, operated,and/or in communication with host vehicle controller 108.

The wireless communication-based autonomous or semi-autonomous vehicletechnology or functionality may include and/or be related to: automaticor semi-automatic steering; automatic or semi-automatic accelerationand/or braking; automatic or semi-automatic blind spot monitoring;automatic or semi-automatic collision warning; adaptive cruise control;and/or automatic or semi-automatic parking assistance. Additionally oralternatively, the autonomous or semi-autonomous technology orfunctionality may include and/or be related to: driver alertness orresponsive monitoring; pedestrian detection; artificial intelligenceand/or back-up systems; navigation or GPS-related systems; securityand/or anti-hacking measures; and/or theft prevention systems.

While host vehicle 100 may be an automobile in the exemplary embodiment,in other embodiments, host vehicle 100 may be, but is not limited to,other types of ground craft, aircraft, and watercraft vehicles.

Exemplary Alert System

FIG. 2 illustrates a simplified block diagram of an exemplary system 200including host vehicle 100 as shown in FIG. 1, and target vehicle (e.g.,second vehicle) 204. In the exemplary embodiment, host vehicle 100includes sensors 102, IA computing device 104, and host vehiclecontroller 108 (all shown in FIG. 1). As explained in FIG. 1, sensors102 on host vehicle 100 detect the presence of a target driver (e.g.,second driver). Sensors 102, such as front view, side view, and/or rearview cameras, detect the position of the target driver to acquirepositional data. Positional data may include the driver's bodyorientation and the location of specific limbs. In the exemplaryembodiment, sensors 102 on host vehicle 100 detect the target driver'sfacial features and body position, such as eye movement, headorientation, neck position, and body posture. In some embodiments,target vehicle 204 may transmit data associated with a target driverand/or target vehicle 204 to sensors 102.

IA computing device 104 receives the sensor data (e.g., second vehicledata) detected by sensors 102, and determines whether target vehicle 204is impaired (e.g., poses a driving hazard) due to an operator impairment(e.g., target driver impairment). IA computing device 104 may comparethe sensor data received from sensors 102 to a baseline model todetermine whether the target driver is impaired. The baseline model maybe configured to include baseline conditions representing safe drivingconditions and standard vehicle maintenance conditions.

The baseline model stored in IA computing device 104 may includebaseline conditions for a range of facial features and body positions inaccordance with safe driving posture. For example, IA computing device104 may determine that the target driver is distracted by comparing thetarget driver's gaze direction and gaze duration to a baseline conditionfor gaze direction stored in IA computing device 104. IA computingdevice 104 may determine that the target driver is impaired if thepositional data received from sensors 102 falls outside one or more ofthe baseline conditions, or is otherwise considered abnormal or anoutlier from expected conditions or data. For example, if a targetdriver falls asleep at the wheel, sensors 102 on host vehicle 100 maydetect the closed eye position, head angle, and head movement of thetarget driver. In this example, IA computing device 104 may compare thepositional data received from sensors 102 to the baseline model anddetermine that the target driver is impaired, or is otherwise associatedwith abnormal driving behavior or activity.

IA computing device 104 may implement computer vision technology and/orother machine learning methods to analyze the sensor data. For instance,deep learning, combined learning, and/or reinforced or reinforcementlearning algorithms or techniques may be applied to the sensor data.

In the exemplary embodiment, IA computing device 104 may be in wirelesscommunication with host vehicle controller 108. When IA computing device104 determines that the target driver is impaired (or otherwiseexhibiting abnormal driving behavior), IA computing device 104 mayinstruct host vehicle controller 108 to generate an alert signal. Hostvehicle controller 108 may subsequently instruct at least one of anauditory signal generator 210, a visual signal generator 212, and ahaptic signal generator 214 to generate an alert signal.

In some embodiments, the alert signal may escalate in frequency as thepotential for a collision increases. For example, red lights may flashon the instrument panel to warn host driver 106 of a potentialcollision. In this example, if host driver 106 takes no action as targetvehicle 204 approaches, the steering wheel and driver's seat maysubsequently vibrate, and continuous alarm sounds may emanate from theaudio system. In some embodiment, the target vehicle 204 mayautomatically engage autonomous or semi-autonomous functionality toavoid a vehicle collision, if so equipped.

In alternative embodiments, IA computing device 104 may communicate witha target vehicle controller 208 of target vehicle 204. In theseembodiments, host vehicle 100 and target vehicle 204 include autonomousor semi-autonomous vehicle-related functionalities, such asvehicle-to-vehicle (V2V) wireless communication, that enable hostvehicle 100 to transmit and receive information. In these embodiments,target vehicle 204 may transmit data as to a target driver and/or targetvehicle 204 to sensors 102 of host vehicle 100. When IA computing device104 determines that the target driver is impaired (or otherwiseexhibiting abnormal driving behavior), IA computing device 104 maytransmit an alert message to target vehicle 204, and warn of a potentialdriving hazard (e.g., collision). IA computing device 104 may instructtarget vehicle controller 208 to generate an alert signal by promptingat least one of auditory signal generator 210, visual signal generator212, and haptic signal generator 214 to alert the target driver. Inthese embodiments, IA computing device 104 transmits alert messages toboth host vehicle controller 108 and target vehicle controller 208 suchthat host driver 106 and the target driver may both take measures toprevent a collision, or that one or both vehicles may automaticallyengage autonomous or semi-autonomous vehicle functionality to avoid acollision if so equipped.

Exemplary Computer-Implemented Method for Detecting an Impairment

FIG. 3 illustrates a flow chart of an exemplary computer-implementedprocess 300 for detecting an impairment. Process 300 may be implementedby a computing device, for example impairment analysis (“IA”) computingdevice 104 (shown in FIG. 1). In the exemplary embodiment, IA computingdevice 104 may be in communication with a user computer device 406(shown in FIG. 4), host vehicle controller 108 (shown in FIG. 1), targetvehicle controller 208 (shown in FIG. 2), an insurer network 408 (shownin FIG. 4), and sensors 102 (shown in FIG. 1).

In the exemplary embodiment, IA computing device 104 may interrogate 302a target vehicle 204 (shown in FIG. 2) by using a plurality of sensors102 (shown in FIG. 1) included on a host vehicle 100 to scan a targetvehicle 204 (shown in FIG. 2) and/or a target driver. Examples ofsensors 102 on host vehicle 100 may include, but are not limited to,front view, side view, and rear view cameras, video devices, LIDAR,radar, and ultrasound.

Sensors 102 may continuously scan surrounding vehicles and drivers ofsurrounding vehicles when host vehicle 100 ignition is on. In theexemplary embodiment, sensors 102 detect oncoming and parallel vehicles.Sensors 102 may scan facial features (e.g., eye position, mouthposition) and upper body positions (e.g., head orientation and angle,neck position) of a target driver. In some embodiments, sensors 102 mayscan additional body positions (e.g., arms, torso) depending on thelocation of sensors 102 in host vehicle 100. In other embodiments, theplurality of sensors 102 on host vehicle 100 includes a wirelesscommunications device.

In the exemplary embodiment, IA computing device 104 may receive 304sensor data. Sensor data may include target driver data associated withthe target driver, and target vehicle condition data associated withtarget vehicle 204. Driver data may include positional data of thetarget driver such as head orientation, body posture, and eye movement.Driver data may also include driving behavior information associatedwith the target driver, including speed, acceleration, gear, braking,vehicle lane maintenance (e.g., lane drifting), vehicle heading anddirection, vehicle operation including vehicle operation with respect toposted speed limits or flow of traffic or environmental conditions, andother conditions related to the operation of target vehicle 204. In someembodiments, IA computing device 104 may receive continuous video datafrom a plurality of cameras.

Target vehicle condition data may include a tread depth of one or morevehicle tires, an environmental sensor reading (e.g., temperature,humidity, and acceleration), vehicle mileage, vehicle oil and fluidlevels, tire pressure, tire temperature, vehicle brake pad thicknesses,gyroscope and accelerometer sensor information. Target vehicle conditiondata may also include information associated with dashboard indicatorlights, engine noise, tire noise, abnormal variation in a dampening of ashock absorber, and the like. Target vehicle condition data may becollected by one or more sensors mounted on or installed within targetvehicle 204.

In certain embodiments where host vehicle 100 and target vehicle 204have autonomous or semi-autonomous vehicle-related functionalities thatenable vehicle-to-vehicle (V2V) communication, IA computing device 104of host vehicle 100 receives the target vehicle condition data fromtarget vehicle 204. In these embodiments, target vehicle 204 maybroadcast an alert signal warning surrounding vehicles, such as hostvehicle 100 that target vehicle 204 and/or the target driver isimpaired. In certain embodiments, the plurality of sensors 102 on hostvehicle 100 may include sophisticated sensing mechanisms that detect oneor more of the vehicle conditions mentioned above.

In the exemplary embodiment, IA computing device 104 may analyze 306 thesensor data by applying a baseline model to the sensor data. In otherembodiments, other models may be applied where these models aregenerated using machine learning and/or artificial intelligencetechniques that are described further herein below. The baseline modelmay include baseline conditions representing safe driving conditions,safe driving behavior, and/or standard vehicle maintenance conditions.

The baseline conditions may represent a range of facial and bodymeasurements in accordance with safe driving. For example, the baselinemodel may include a range of head motions for an average adult ofvarying heights. The range of head motions may encompass measurementsfor horizontal head rotation (e.g., right to left rotation of the head)and movements of the neck (e.g., forward to backward movement, right toleft movement) associated with safe driving behavior.

The baseline model may also include baseline conditions for laneposition, speed, and vehicle dynamics. In some embodiments, the baselineconditions for a vehicle or for vehicle operation may be dynamic andchange based upon location and driving conditions, includingenvironmental, traffic density, and/or construction. For instance, GPSlocation may be used to determine a posted speed limit and a directionof traffic. The plurality of sensors 102 on host vehicle 100 may be usedto determine weather conditions, such as heavy rain, light rain, ice,snow, or sleet, or absence thereof. The plurality of sensors 102 mayalso be used to determine traffic density, flow of traffic, speed oftraffic, etc. The baseline conditions may include parameters as tonormal direction of traffic flow, and normal traffic stoppage, such asat a traffic light or stop sign.

During the analysis process, IA computing device 104 compares the sensordata to the baseline conditions of the baseline model to determinewhether the target driver and/or target vehicle 204 is impaired, orotherwise exhibiting abnormal vehicle operation. In some embodiments, IAcomputing device 104 compares the sensor data to one or more baselineconditions. The baseline conditions may include parameters associatedwith safe driving and standard vehicle maintenance. IA computing device104 may continuously apply baseline conditions of the baseline model inreal time as the IA computing device 104 receives 304 the sensor data.In certain embodiments, comparing the sensor data to the baseline modelmay reveal one or more outliers (e.g., abnormalities, deviations) fromexpected conditions (e.g., the baseline conditions). In otherembodiments, IA computing device 104 selects the baseline conditions touse based upon the type of sensor data the IA computing device 104receives. For example, IA computing device 104 may use baselineconditions for eye movement and head position to compare positional datareceived from sensors 102. Additionally or alternatively, IA computingdevice 104 may use baseline conditions for vehicle dynamics to comparesteering input information received from sensors 102. Such data mayindicate swerving or vehicle operation outside of a designated lane. Insome embodiments, the baseline model may include data from the NationalTransportation Safety Board or the National Highway Traffic SafetyAdministration.

In further embodiments, the baseline conditions may include thresholdssuch as a first threshold and a second threshold for assessing thesensor data. The thresholds may be predetermined thresholds stored indatabase 404 (see FIG. 4). One or more thresholds may be used tocategorize the sensor data received (such as data on vehicle speedvariation and/or vehicle lane departure) as low (or no) impairment,medium impairment, or high impairment. For example, IA computing device104 may determine target vehicle 204 is traveling at an inconsistentspeed (e.g., accelerating or decelerating in a relatively short periodof time). The sensor data may indicate that target vehicle 204accelerated 15 miles per hour (mph) in a short amount of time. However,IA computing device 104 may determine that the acceleration is not animpairment (e.g., not a risk to host vehicle 100) because prior toaccelerating, target vehicle 204 was at a red traffic light. In thisexample, although the 15 mph acceleration may exceed a first thresholdapplied by IA computing device 104, IA computing device may determinethat, given the circumstances, the acceleration is not enough to exceeda second threshold because target vehicle 204 is traveling at relativelythe same speed as nearby vehicles.

In another example, IA computing device 104 may apply a first thresholdand determine that target vehicle 204 is drifting from its associatedlane. In this example, target vehicle 204 may be traveling in the leftlane alongside host vehicle 100. IA computing device 104 may apply asecond threshold to determine if the lane deviation (e.g., lanedeparture) of target vehicle 204 presents a risk (e.g., impairment) tohost vehicle 100. For instance, if the target vehicle 204 immediatelyrealigns itself along the center of its lane (e.g., target vehicle 204has an automatic or semi-automatic lane-keeping assist system), IAcomputing device 104 may determine that the detected lane departure oftarget vehicle 204 presents no or low impairment to host vehicle 100.

However, if target vehicle 204 continues to drift outside of itsassociated lane markings, IA computing device 104 may determine that thedetected lane departure of target vehicle 204 presents an impairment. IAcomputing device 104 may categorize the impairment as medium or highimpairment depending on factors such as the time period and/or extent ofthe lane departure (e.g., swerving in and out of lane multiple times).

In the exemplary embodiment, IA computing device 104 may detect 308 animpairment based upon the analysis. The impairment may be an operatorimpairment associated with the target driver and/or a vehicle impairmentof target vehicle 204. For example, based upon the sensor data, IAcomputing device 104 may detect that the target driver is distracted andthat one of the tires of target vehicle 204 is flat.

In some embodiments, IA computing device 104 may compare the sensor datato the baseline model and determine whether the sensor data meets, orexceeds, one or more baseline conditions. For example, IA computingdevice 104 may determine that target vehicle 204 is impaired bydetecting that target vehicle 204 is traveling at a speed outside arange set by the baseline condition, which may be associated with aposted speed limit for a given GPS location or road. In otherembodiments, IA computing device 104 may compare the sensor data to thebaseline model and determine whether the sensor data exceeds a firstthreshold.

IA computing device 104 may categorize the sensor data as low impairmentif the sensor data does not exceed the first threshold. In theseembodiments, IA computing device 104 may determine if the sensor dataexceeds multiple thresholds (e.g., second threshold, third threshold),and categorize the sensor data accordingly as low, medium, or highimpairment. For example, if a target driver is actively talking to apassenger, IA computing device 104 may analyze the target driver's eyeor mouth movement and head position, and categorize the sensor data ashigh impairment. However, if the target driver is talking to a passengerwhile waiting at a red light, IA computing device 104 may categorize thesensor data as low impairment.

In the exemplary embodiment, IA computing device 104 may output 310 analert signal based upon the determination that target vehicle 204 isimpaired. In some embodiments, IA computing device 104 may determinethat target vehicle 204 is impaired if IA computing device 104 detectsan impairment that is categorized as a medium or high impairment. Inthese embodiments, IA computing device 104 may not output an alertsignal for impairments categorized as low impairments. In otherembodiments, IA computing device 104 may determine that target vehicle204 is impaired if the sensor data meets or falls outside the baselineconditions of the baseline model.

In the exemplary embodiment, IA computing device 104 outputs the alertsignal to target vehicle 204. The alert signal is at least one of anauditory signal, a visual signal and a haptic signal. In someembodiments, IA computing device 104 may output multiple alert signalsdepending on a category of impairment (e.g., high impairment). Forexample, if target vehicle 204 speeds through a red light and headsdirectly towards host vehicle 100, IA computing device 104 maysimultaneously output an auditory, visual, and haptic signal to alerthost driver 106 (shown in FIG. 1).

In some embodiments, IA computing device 104 may include host vehiclecontroller 108 (shown in FIG. 2). In other embodiments, IA computingdevice 104 may separately instruct host vehicle controller 108 to promptat least one of auditory signal generator 210, visual signal generator212, and haptic signal generator 214 to output an alert signal.

Additionally or alternatively, in embodiments where host vehicle 100 hasautonomous or semi-autonomous vehicle related functionalities, IAcomputing device 104 may engage in other corrective action (e.g.,collision avoidance action) based upon detecting that target driverand/or target vehicle 204 is impaired. In some embodiments, IA computingdevice 104 may generate a recommendation or a control signal at hostvehicle 100 to engage an automatic safety system (e.g., autonomousvehicle control system) of host vehicle 100, such as an automaticbraking system (e.g., automatic emergency braking), acceleration system,and/or steering system, if so equipped. In other embodiments, IAcomputing device 104 may generate a recommendation or a control signalat host vehicle 100 to engage a semi-automatic safety system (e.g.,semi-autonomous vehicle control system) of host vehicle 100, such as asemi-automatic braking system, acceleration system, and/or steeringsystem.

In certain embodiments where host vehicle 100 has a variety of automated(e.g., semi-automatic, automatic) safety systems, IA computing device104 may recommend engaging (or cause to be engaged) one or moreautomated safety systems that are best suited to prevent a collisionwith target vehicle 204 (e.g., reduce a probability of colliding withtarget vehicle 204) given the circumstances surrounding the detectedimpairment. For example, if IA computing device 104 detects animpairment due to target vehicle 204 speeding through a red light ashost vehicle 100 makes a left turn, IA computing device 104 mayrecommend engaging (or cause to be engaged) an automated steering orbraking system (e.g., automatic emergency braking system) of hostvehicle 100 rather than engaging a blind spot warning system. In furtherembodiments, IA computing device 104 may instruct host vehiclecontroller 108 to automatically steer host vehicle 100 away from anoncoming path of target vehicle 204. In these embodiments, host vehiclecontroller 108 may subsequently instruct a vehicle control system suchas a vehicle steering system to maneuver host vehicle 100 away fromtarget vehicle 204.

IA computing device 104 may convey the recommendation to host driver 106via auditory signal generator 210 and/or visual signal generator 212. Inthese embodiments, IA computing device 104 may take corrective actionbased upon feedback from host driver 106 in regards to therecommendation. For example, IA computing device 104 may recommendengaging an automated braking system of host vehicle 100. Therecommendation may be audibly conveyed to host driver 106 via auditorysignal generator 210, and may require a response input from host driver106 to engage the automated braking system. In further embodiments wherehost vehicle 100 has autonomous vehicle related functionalities, IAcomputing device 104 may automatically engage an automated (e.g.,automatic or semi-automatic) safety system such as an automatic brakingsystem, acceleration system, and/or steering system, if so equipped. Inthese embodiments, IA computing device 104 may engage an automatedsafety system of host vehicle 100 without requiring any input from hostdriver 106.

In further embodiments where host vehicle 100 and target vehicle 204have autonomous or semi-autonomous vehicle related functionalities thatenable vehicle-to-vehicle (V2V) communication, IA computing device 104may output the alert signal to target vehicle 204 (as shown in FIG. 2).More specifically, IA computing device 104 may transmit an alert messageto target vehicle controller 208 (as shown in FIG. 2), and targetvehicle controller 208 may prompt at least one of auditory signalgenerator 210, visual signal generator 212, and/or haptic signalgenerator 214 of target vehicle 204 to output an alert signal. Incertain embodiments, transmitting an alert message to target vehiclecontroller 208 may result in target vehicle controller 208 takingcontrol of target vehicle 204 to prevent a driving hazard (e.g.,actively steer target vehicle 204 and prevent target vehicle 204 fromleaving its lane, or automatically engaging other autonomous orsemi-autonomous vehicle technologies).

In some further embodiments, IA computing device 104 may store thebaseline model, including the baseline conditions, in at least onememory device. In other embodiments, IA computing device 104 may storethe sensor data in at least one memory device. In these embodiments, IAcomputing device may transmit the sensor data to a remote-computingdevice to update at least one of an underwriting model and an actuarialmodel. The sensor data may be used to adjust an insurance policy of aninsurance holder, such as providing an increased discount for having avehicle equipped with the collision avoidance functionality discussedherein.

Exemplary Computer Network

FIG. 4 depicts a simplified block diagram of an exemplary computersystem 400 for implementing process 300 shown in FIG. 3 and process 700shown in FIG. 7A. In the exemplary embodiment, computer system 400 maybe used to determine that target vehicle 204 is impaired (e.g., poses adriving hazard) or otherwise operating abnormally or erratically due toan operator impairment and/or a vehicle impairment. As described belowin more detail, impairment analysis (“IA”) computing device 104, whichis implemented locally on host vehicle 100 (shown in FIG. 1) may beconfigured to (i) interrogate target vehicle 204 (shown in FIG. 2) byusing a plurality of sensors included on host vehicle 100 to scan targetvehicle 204 and a target driver; (ii) receive sensor data includingtarget driver data and/or target vehicle condition data; (iii) analyzethe sensor data by applying a baseline model to the sensor data; (iv)detect an impairment based upon the analysis; and/or (v) output an alertsignal based upon detecting that target vehicle 204 (shown in FIG. 2) isimpaired (e.g., poses a driving hazard), or direct other collisionavoidance actions.

In the exemplary embodiment, IA computing device 104 is in communicationwith sensors 102 (as shown in FIG. 1). Sensors 102 may be positioned onhost vehicle 100 (shown in FIG. 2), and include at least one of radar,LIDAR, Global Positioning System (GPS), video recording devices, imagingdevices, cameras, and audio records. Sensors 102 may be any devicecapable of scanning the head, face (including eyes), and body of thetarget driver. Sensors 102 may also be any device capable of detectingmovements of target vehicle 204 including at least one of steering wheelangle, speed, acceleration, lane deviation, and road condition. In someembodiments, sensors 102 include a wireless communication device. Inthese embodiments, sensor data may be received at the wirelesscommunication device as an alert message from target vehicle 204.

In the exemplary embodiment, user computer devices 406 may be computersthat include a web browser or a software application, which enables usercomputer devices 406 to access remote computer devices, such as IAcomputing device 104 and insurer network 408 computer devices, using theInternet or other network. More specifically, user computer devices 406may be communicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and a cablemodem. User computer devices 406 may be any device capable of accessingthe Internet including, but not limited to, a desktop computer, a laptopcomputer, a personal digital assistant (PDA), a cellular phone, asmartphone, a tablet, a phablet, wearable electronics, smart watch, orother web-based connectable equipment or mobile devices.

In the exemplary embodiment, a database server 402 may becommunicatively coupled to a database 404 that stores data. Database 404may be located on host vehicle 100 or may be located remotely from hostvehicle 100. In one embodiment, database 404 may include the sensor data(e.g., second vehicle data) received from sensors 102, the baselinemodel including the baseline conditions, and the processed sensor datato which the baseline model has been applied. In the exemplaryembodiment, database 404 may be stored remotely from IA computing device104. In some embodiments, database 404 may be decentralized. In otherembodiments, a user, such as host driver 106 (shown in FIG. 1), mayaccess database 404 via a user computer device 406 by logging into IAcomputing device 104.

IA computing device 104 may also be communicatively coupled with hostvehicle controller 108 (as shown in FIG. 1). In some embodiments, IAcomputing device 104 may include host vehicle controller 108.Additionally or alternatively, IA computing device may becommunicatively coupled with target vehicle controller 208 (as shown inFIG. 2). In these embodiments, host vehicle 100 and target vehicle 204(both shown in FIG. 2) have autonomous or semi-autonomousvehicle-related functionalities enabling vehicle-to-vehicle (V2V)communication. IA computing device 104 may be communicatively coupledwith one or more user computing devices 406.

In some embodiments, IA computing device 104 may be associated with, oris part of a computer network associated with an insurance provider, orin communication with the insurance network 408 computer devices. Inother embodiments, IA computing device 104 may be associated with athird party and is merely in communication with the insurance network408 computer devices. More specifically, IA computing device 104 iscommunicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and a cablemodem.

Insurer network 408 computer devices may include one or more computerdevices associated with an insurance provider. In some embodiments, aninsurance provider may be associated with a user, such as host driver106 (shown in FIG. 1) and/or a target driver who has an auto insurancepolicy with insurance provider. In these embodiments, insurer network408 computer devices may include a web browser or a softwareapplication, which enables insurer network 408 computer devices toaccess remote computer devices, such as IA computing device 104 anddatabase server 402, using the Internet or other network. Morespecifically, insurer network 408 computer devices may becommunicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and a cablemodem.

User computer devices 406 may be any device capable of accessing theInternet including, but not limited to, a desktop computer, a laptopcomputer, a personal digital assistant (PDA), a cellular phone, asmartphone, a tablet, a phablet, wearable electronics, smart watch, orother web-based connectable equipment or mobile devices. In someembodiments, insurer network 408 computer devices may access database404 to update an underwriting model and/or an actuarial model. In otherembodiments, insurer network 408 computer devices may access database404 to adjust an insurance policy of an insurance holder (e.g., targetdriver). Moreover, insurer network 408 computer devices may specificallyaccess database 404 to collect real-time data on impaired drivers and/orimpaired vehicles.

In some embodiments, IA computing device 104 may transmit the sensordata to a remote-computing device such as insurer network 408 computerdevices. The transmitted sensor data may be used to update and/or createan underwriting model and/or an actuarial model to determine thelikelihood of vehicle collisions on roads due to impaired drivers and/orimpaired vehicles. In other embodiments, the sensor data may be used toadjust an insurance policy of an insurance policy holder. For example,following a vehicle collision, the sensor data may be transmitted to atarget driver's insurer network 408 computer device, and used to preparea proposed insurance claim for an insured's review and/or ultimatelyadjust the target driver's insurance policy.

Insurer network 408 computer devices may retrieve, from IA computingdevice 104, sensor data such as (but not limited to) the weatherconditions, the information on the roads and road conditions, the speedlimits on those roads, estimated traffic patterns on those roads duringvarious dates and times, such as weekend/weekday/holiday trafficpatterns, the make, model, and year of target vehicle 204, and/or safetyfeatures of target vehicle 204. Insurer network 408 computer devices mayalso retrieve sensor data that includes information on surroundingaccidents and/or traffic conditions on the roads. The road informationmentioned above and elsewhere herein may be retrieved in someembodiments based upon current GPS location of either the host or targetvehicle being used to identify the relevant road or roads that the hostor target vehicles are traveling. In some embodiments, IA computingdevice 104 may transmit sensor data that enables insurer network 408computer devices to determine the safety record of target vehicle 204.

This data may subsequently be analyzed by insurer network 408 computerdevices using modeling techniques such as artificial intelligence,character recognition (e.g., through use of computer vision), or machinelearning. These modeling techniques may be used to determine whichcircumstances are most indicative of high-risk impaired driving. Thesedeterminations may subsequently be used to review and adjust automobileinsurance premiums of drivers. For example, drivers who have longcommutes everyday on congested roads may have higher insurance premiumsthan those who drive short distances a several times a week with lightertraffic density.

In some embodiments, the transmitted sensor data may include datareceived by IA computing device 104 from an autonomous orsemi-autonomous vehicle. The types of autonomous or semi-autonomousvehicle-related functionality or technology that may be used with thepresent embodiments include and/or be related to the following types offunctionality: (a) fully autonomous (driverless); (b) limited drivercontrol; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering, acceleration, braking,collision warning, and/or blind spot monitoring; (f) adaptive cruisecontrol; (g) automatic or semi-automatic parking/parking assistanceand/or collision preparation (windows roll up, seat adjusts upright,brakes pre-charge, etc.); (h) driver acuity/alertness monitoring; (i)pedestrian detection; (j) autonomous or semi-autonomous backup systems;(k) road mapping or navigation systems; (l) software security andanti-hacking measures; (m) theft prevention/automatic return; (n)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

The data received by IA computing device 104 from autonomous orsemi-autonomous vehicles may include further indicators representativeof high-risk impaired driving in addition to those received fromentirely manually-operated vehicles. For example, the data may includedetailed information collected by target vehicle 204 as to a targetdriver and the vehicle condition of target vehicle 204. For example,based upon the type of autonomous or semi-autonomous vehicle-relatedtechnology of target vehicle 204, the data may include driving history(e.g., number of accidents and/or near accidents at a specific time,date, and/or geographical location) and/or driving pattern of a targetdriver. Insurer network 408 computer devices may retrieve this data fromIA computing device 104. This data may subsequently be analyzed byinsurer network 408 computer devices using modeling techniques, such asartificial intelligence and/or machine learning. These modelingtechniques may be used to determine which circumstances are mostindicative of high-risk impaired driving to review and adjust automobileinsurance premiums of drivers.

The adjustments to automobile insurance rates or premiums based upon theautonomous or semi-autonomous vehicle-related functionality ortechnology may take into account the impact of such functionality ortechnology on the likelihood of a vehicle accident or collisionoccurring. For instance, a processor may analyze historical accidentinformation and/or test data involving vehicles having autonomous orsemi-autonomous functionality. Factors that may be analyzed and/oraccounted for that are related to insurance risk, accident information,or test data may include (a) point of impact; (b) type of road; (c) timeof day; (d) weather conditions; (e) road construction; (f) type/lengthof trip; (g) vehicle style; (h) level of pedestrian traffic; (i) levelof vehicle congestion; (j) atypical situations (such as manual trafficsignaling); (k) availability of internet connection for the vehicle;and/or other factors. These types of factors may also be weightedaccording to historical accident information, predicted accidents,vehicle trends, test data, and/or other considerations.

Insurance network 408 computer devices may use machine learning and/orartificial intelligence techniques to develop impaired driving modelsthat can be used for adjusting and/or calculating automobile insurancepremiums based upon risks associated with certain driving situations. Insome embodiments, insurance network 408 computer devices may create orupdate one or more impaired driving models based upon the sensor datareceived from the plurality of sensors 102 on host vehicle 100. Sensordata generally may include target driver data and target vehiclecondition data, such as (but not limited to), suddenacceleration/deceleration, average speed, and average stopping distance,as well as times of the day/week target vehicle 204 is driven, the modeland make of target vehicle 204, distance driven, and/or locationinformation.

IA computing device 104, host vehicle controller 108, and/or usercomputer device 406 may employ machine learning functionality to developand maintain impaired driving models that characterize the driving ofimpaired drivers and the vehicle condition of impaired vehicles basedupon sensor data including target driver data and target vehiclecondition data, such that IA computing device 104 may continually updatethe impaired driver models. For example, a target driver may exhibit oneor more high-risk behaviors, according to collected vehicle telematicsdata (e.g., high occurrence of abrupt deceleration, particularly fastturns, and/or extreme acceleration).

The impaired driving models may include one or more characteristics thatrepresent various risk behaviors exhibited (or not exhibited) by atarget driver and/or target vehicle 204. For instance, onecharacteristic may include a numeric value or other indicator thatrepresents that a target driver rarely drives above a posted speedlimit. As another example, another characteristic may include a numericvalue or other indicator that represents that a target driver frequentlydrives at “high-risk” times of the day, such as between midnight and 6AM. As another example, a characteristic may be associated withmaintenance of target vehicle 204, such as whether or not scheduledmaintenance is performed in a timely manner.

IA computing device 104 may be any device capable of accessing theInternet including, but not limited to, a desktop computer, a laptopcomputer, a personal digital assistant (PDA), a cellular phone, asmartphone, a tablet, a phablet, wearable electronics, smart watch, orother web-based connectable equipment or mobile devices. In stillfurther embodiments, IA computing device 104 may be separate from hostvehicle controller 108 (as shown in FIG. 1) and merely be incommunication with host vehicle controller 108 to transmit instructionsfor generating an alert signal, or directing other collision avoidanceactions.

Exemplary Client Device

FIG. 5 depicts an exemplary configuration of user computer device 406shown in FIG. 4, in accordance with one embodiment of the presentdisclosure. User computer device 502 may be operated by a user 504. Usercomputer device 502 may include, but is not limited to, user computerdevices 406, IA computing device 104, host vehicle controller 108, andtarget vehicle controller 208 (all shown in FIG. 4).

User computer device 502 may include a processor 506 for executinginstructions. In some embodiments, executable instructions may be storedin a memory area 508. Processor 506 may include one or more processingunits (e.g., in a multi-core configuration). Memory area 508 may be anydevice allowing information such as executable instructions and/ortransaction data to be stored and retrieved. Memory area 508 may includeone or more computer readable media.

User computer device 502 may also include at least one media outputcomponent 510 for presenting information to user 504. Media outputcomponent 510 may be any component capable of conveying information,such as an alert signal to user 504. In some embodiments, media outputcomponent 510 may include an output adapter (not shown) such as a videoadapter and/or an audio adapter. An output adapter may be operativelycoupled to processor 506 and operatively coupleable to an output device,such as a display device (e.g., a cathode ray tube (CRT), liquid crystaldisplay (LCD), light emitting diode (LED) display, or “electronic ink”display) or an audio output device (e.g., a speaker or headphones).

In some embodiments, media output component 510 may be configured topresent a graphical user interface (e.g., a web browser and/or a clientapplication) to user 504. A graphical user interface may include, forexample, an interface for viewing visual alerts. In some embodiments,user computer device 502 may include an input device 512 for receivinginput from user 504. User 504 may use input device 512 to, withoutlimitation, preset alert signal settings on host vehicle 100 and/oracknowledge a visual alert/message.

Input device 512 may include, for example, a keyboard, a pointingdevice, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad ora touch screen), a gyroscope, an accelerometer, a position detector, abiometric input device, and/or an audio input device. A single componentsuch as a touch screen may function as both an output device of mediaoutput component 510 and input device 512.

User computer device 502 may also include a communication interface 514,communicatively coupled to a remote device such as IA computing device104 (shown in FIG. 1). Communication interface 514 may include, forexample, a wired or wireless network adapter and/or a wireless datatransceiver for use with a mobile telecommunications network.

Stored in memory area 508 are, for example, computer readableinstructions for providing a user interface to user 504 via media outputcomponent 510 and, optionally, receiving and processing input from inputdevice 512. A user interface may include, among other possibilities, aweb browser and/or a client application. Web browsers enable users, suchas user 504, to display and interact with media and other informationtypically embedded on a web page or a website from IA computing device104.

A client application may allow user 504 to interact with, for example,IA computing device 104. For example, instructions may be stored by acloud service, and the output of the execution of the instructions sentto the media output component 510.

Exemplary Server Device

FIG. 6 depicts an exemplary configuration 600 of a server computerdevice 602, in accordance with one embodiment of the present disclosure.In the exemplary embodiment, server computer device 602 may be similarto, or the same as, IA computing device 104 (shown in FIG. 1). Servercomputing device 602 may include, but is not limited to, IA computingdevice 104, insurer network 408 computer devices, and database server402 (all shown in FIG. 4). Server computer device 602 may also include aprocessor 604 for executing instructions. Instructions may be stored ina memory area 606. Processor 604 may include one or more processingunits (e.g., in a multi-core configuration).

Processor 604 may be operatively coupled to a communication interface608 such that server computer device 602 is capable of communicatingwith a remote device such as another server computer device 602, IAcomputing device 104, host vehicle controller 108, target vehiclecontroller 208, and user computer devices 406 (all shown in FIG. 4) (forexample, using wireless communication or data transmission over one ormore radio links or digital communication channels). For example,communication interface 608 may receive requests from user computerdevices 406 via the Internet or other network, as illustrated in FIG. 4.

Processor 604 may also be operatively coupled to a storage device 610.Storage device 610 may be any computer-operated hardware suitable forstoring and/or retrieving data, such as, but not limited to, dataassociated with database 404 (shown in FIG. 4). In some embodiments,storage device 610 may be integrated in server computer device 602. Forexample, server computer device 602 may include one or more hard diskdrives as storage device 610.

In other embodiments, storage device 610 may be external to servercomputer device 602 and may be accessed by a plurality of servercomputer devices 602. For example, storage device 610 may include astorage area network (SAN), a network attached storage (NAS) system,and/or multiple storage units such as hard disks and/or solid statedisks in a redundant array of inexpensive disks (RAID) configuration.

In some embodiments, processor 604 may be operatively coupled to storagedevice 610 via a storage interface 612. Storage interface 612 may be anycomponent capable of providing processor 604 with access to storagedevice 610. Storage interface 612 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 604with access to storage device 610.

Processor 604 may execute computer-executable instructions forimplementing aspects of the disclosure. In some embodiments, theprocessor 604 may be transformed into a special purpose microprocessorby executing computer-executable instructions or by otherwise beingprogrammed. For example, the processor 604 may be programmed with theinstruction such as illustrated in FIG. 3.

A client application may allow user 504 to interact with, for example,IA computing device 104. For example, instructions may be stored by acloud service, and the output of the execution of the instructions sentto the media output component 510.

Exemplary Computer-Implemented Method for Alerting a Host Driver to aDriving Hazard

FIG. 7A illustrates a flow chart of an exemplary computer-implementedprocess 700 for one aspect of determining whether target vehicle 204poses a driving hazard to host vehicle 100 (e.g., is impaired) usingsystem 400 (shown in FIG. 4). Process 700 may be implemented by acomputing device, for example impairment analysis (“IA”) computingdevice 104 (shown in FIG. 1). In the exemplary embodiment, IA computingdevice 104 may be in communication with a user computer device 406(shown in FIG. 4), host vehicle controller 108, target vehiclecontroller 208, an insurer network 408, and sensors 102 (all shown inFIG. 4).

In the exemplary embodiment, IA computing device 104 may receive 702second vehicle data (e.g., sensor data) including second driver data(e.g., target driver data) and second vehicle condition data (e.g.,target vehicle condition data) of a second vehicle (e.g., target vehicle204), that is collected by a plurality of sensors 102 included on afirst vehicle (e.g., host vehicle 100). Sensors 102 such as front view,side view, and rear view cameras, LIDAR, radar, and ultrasound maycontinuously scan vehicles of oncoming and parallel traffic when thefirst vehicle's ignition is on.

Sensors 102 may scan the facial features (e.g., eye movement) and bodypostures (e.g., head orientation, neck position) of a second driver(e.g., target driver) to capture second driver data and second vehiclecondition data. The plurality of sensors 102 on the first vehicle mayinclude a wireless communications device. Second driver data may includepositional data of the second driver such as head orientation, bodyposture and orientation, head or mouth movement, arm movement, and eyemovement. Second driver data may also include driving behaviorinformation associated with the second driver, including speed,acceleration, gear braking, vehicle orientation and direction, andvehicle lane maintenance (e.g., lane drifting), and other conditionsrelated to the operation of the second vehicle (e.g., target vehicle204). In some embodiments, IA computing device 104 may receive videodata from a plurality of cameras.

Second vehicle condition data may include a tread depth of one or morevehicle tires, an environmental sensor reading (e.g., temperature,humidity, and acceleration), vehicle mileage, vehicle oil and fluidlevels, tire pressure, tire temperature, vehicle brake pad thicknesses,gyroscope and accelerometer sensor information. Second vehicle conditiondata may also include information associated with vehicle maintenance,engine noise, tire noise, abnormal variation in a dampening of a shockabsorber, and the like. Second vehicle condition data may be collectedby one or more sensors mounted on or installed within the secondvehicle. Second vehicle condition data may include operation dataassociated with the operation or operability of one or more safetyfeatures, including one or more autonomous or semi-vehicle systems ortechnologies, and associated maintenance records. In certain embodimentswhere first vehicle 100 and second vehicle 204 have autonomous orsemi-autonomous vehicle-related functionalities that enablevehicle-to-vehicle (V2V) communication, IA computing device 104 of thefirst vehicle receives the second vehicle condition data from the secondvehicle. In other embodiments, the plurality of sensors 102 on the firstvehicle may include sophisticated sensing mechanisms that detect one ormore of the vehicle conditions mentioned above.

IA computing device 104 may analyze 704 the second vehicle data byapplying a baseline model to the second vehicle data. The baselineconditions may represent a range of facial and body measurements inaccordance with safe driving. For example, the baseline model mayinclude a range of head motions for an average adult of varying heights.The range of head motions may encompass measurements for horizontal headrotation (e.g., right to left rotation of the head) and movements of theneck (e.g., forward to backward movement, right to left movement)associated with standard driving posture. The baseline model may alsoinclude baseline conditions for lane position, speed, and vehicledynamics.

During the analysis process, IA computing device 104 may compare thesecond vehicle data to the baseline conditions of the baseline model todetermine whether the second driver and/or the second vehicle pose adriving hazard to the first vehicle. For instance, the second vehicledata may reveal one or more outliers from the baseline model or abnormalconditions. In some embodiments, IA computing device 104 compares thesecond vehicle data to one or more baseline conditions.

In some embodiments, IA computing device 104 selects the baselineconditions to use based upon the type of second vehicle data the IAcomputing device 104 receives. For example, IA computing device 104 mayuse baseline conditions for eye movement and head position to comparepositional data received from sensors 102. In further embodiments, thebaseline model may include baseline conditions that enable IA computingdevice 104 to compare a driver's head or view angle to reference samplesstored in database 404. For example, a candidate head placement (e.g.,of a first driver of a first vehicle) may be compared, by IA computingdevice 104 to a set of head positions and head angles associated with analert driver of a specific body frame (e.g., height). Additionally oralternatively, IA computing device 104 may use baseline conditions forvehicle dynamics to compare steering input information received fromsensors 102. In some embodiments, the baseline model may include datafrom the National Transportation Safety Board or the National HighwayTraffic Safety Administration.

IA computing device 104 may also determine 706 that the second vehicleposes a driving hazard (e.g., impairment) to the first vehicle basedupon the analysis. The driving hazard may be due to an operatorimpairment of the second driver and/or a vehicle impairment of thesecond vehicle. IA computing device 104 may compare the second vehicledata to the baseline model and determine whether the second vehicle datameets or exceeds one or more baseline conditions. For example, IAcomputing device 104 may determine that the second vehicle is impaired,or otherwise operating abnormally, by detecting that the second vehicleis traveling at a speed outside a range set by the baseline condition,such as outside speed range established by traffic flow, trafficdensity, type or road, or posted speed limit.

In other embodiments, IA computing device 104 may compare the secondvehicle data to the baseline model and determine whether the secondvehicle data exceeds a first threshold, such as a speed threshold for agiven location or posted speed limit. IA computing device 104 maycategorize the second vehicle data as a low driving hazard if the secondvehicle data does not exceed the first threshold. In these embodiments,IA computing device 104 may determine if the second vehicle data exceedsmultiple thresholds (e.g., second threshold, third threshold), andcategorize the second vehicle data accordingly as a low, medium, or highdriving hazard.

IA computing device may generate 708 an alert signal based upon thedetermination that the second vehicle poses a driving hazard to thefirst vehicle. IA computing device 104 may determine that the secondvehicle poses a driving hazard to the first vehicle if IA computingdevice 104 detects second vehicle data that is categorized as a mediumor high driving hazard. In these embodiments, IA computing device 104may not output an alert signal for second vehicle data categorized aslow driving hazards. In other embodiments, IA computing device 104 maydetermine that the second vehicle poses a driving hazard if the secondvehicle data meets or falls outside the baseline conditions of thebaseline model. In the exemplary embodiment, IA computing device 104 maygenerate the alert signal to first vehicle by transmitting the alertsignal to a first vehicle controller (e.g., similar to host vehiclecontroller 108 as shown in FIG. 1).

The first vehicle controller may instruct one or more of an auditorysignal generator, visual signal generator, and haptic signal generatorto output an auditory, visual, and/or haptic warning alert (similar toFIG. 2). In some embodiments, IA computing device 104 may outputmultiple alert signals depending on how the second vehicle data iscategorized (e.g., high driving hazard). IA computing device 104 maysimultaneously output an auditory, visual, and haptic signal to alertthe first driver.

In certain embodiments where first vehicle and second vehicle haveautonomous or semi-autonomous vehicle related functionalities thatenable vehicle-to-vehicle (V2V) communication, IA computing device 104may generate the alert signal to the second vehicle by sending an alertmessage through the V2V communication network. More specifically, IAcomputing device 104 may transmit an alert message to second vehiclecontroller (similar to target vehicle controller 208 as shown in FIG.2), which may prompt at least one of auditory signal generator 210,visual signal generator 212, and haptic signal generator 214 of thesecond vehicle to generate a warning alert to warn the second driver.

In some further embodiments, IA computing device 104 may store thebaseline model in at least one memory device. In these embodiments, thebaseline model may include the baseline conditions representing safedriving conditions and standard vehicle maintenance conditions. In otherembodiments, IA computing device 104 may store the second vehicle datain at least one memory device. In these embodiments, IA computing devicemay transmit the second vehicle data to a remote-computing device toupdate at least one of an underwriting model and an actuarial model. Thesecond vehicle data may be used to adjust an insurance policy of aninsurance holder such as the insurance policy of a target driver. Incertain embodiments, the sensor data may be used to determine thestatistics surrounding impaired drivers and/or impaired vehicles basedupon factors such as location (e.g., city, suburb, rural area, urbanarea), time of day (e.g., morning, midnight), day of week (e.g.,weekend, weekday, holiday), vehicle makes and types (e.g., sportsvehicles, trucks, minivans), and traffic patterns (e.g., rush hour).Modeling data may be extrapolated from the sensor data to evaluate riskassociated with impaired drivers and/or impaired vehicles.

FIG. 7B illustrates a flow chart of another exemplarycomputer-implemented process 750 for one aspect of determining whetherhost vehicle 100 (e.g., first vehicle) poses a driving hazard to targetvehicle 204 (e.g., second vehicle) using system 400 (shown in FIG. 4).Process 750 may be implemented by a computing device, for exampleimpairment analysis (“IA”) computing device 104 (shown in FIG. 1). Inparticular, the first vehicle and the second vehicle as illustrated byFIG. 7B possess autonomous or semi-autonomous technology orfunctionality that enables the first vehicle to communicate with thesecond vehicle.

In the exemplary embodiment, IA computing device 104 may gather 752sensor data associated with the first vehicle (such as host vehicle 100)and/or a first driver (e.g., host driver 106) of the first vehicle viavehicle-mounted sensors. The sensor data may include image, audio,telematics (vehicle speed, braking, direction, cornering, etc.), andother sensor data associated with the first vehicle and/or the firstdriver. The sensor data may further include data as to the environmentsurrounding the first vehicle (e.g., environmental data) such as imagesof surrounding vehicles, pedestrians, roads, road signs, and/or trafficlights, as well as data associated with traffic conditions, roadconditions, weather conditions, time of day, and/or location.

IA computing device 104 may analyze 754 the sensor data of the firstvehicle and/or the first driver to determine one or more outliers (e.g.,abnormalities, deviations) from expected conditions (e.g., baselineconditions). IA computing device 104 may continuously apply a baselinemodel to the sensor data to determine whether one or more outliers fromthe baseline model are present within the sensor data. The sensor datamay include data as to lane maintenance (e.g., lane departure, lanedeviation) and vehicle speed maintenance (e.g., variation in speed). Thebaseline model may include baseline conditions and/or data representinga range of parameters for lane position, lane maintenance, vehiclespeed, and vehicle dynamics.

The baseline model may include thresholds to assess sensor data as tovehicle speed maintenance (and/or acceleration maintenance) and lanemaintenance. For example, IA computing device 104 may determine whethera vehicle speed associated with the first vehicle remains relativelyconsistent (e.g., maintaining speed between 55-60 mph) or fluctuatesrapidly (e.g., accelerating or decelerating in a relatively short periodof time). IA computing device 104 may further determine whether thevehicle speed of the first vehicle presents a risk (e.g., presents animpairment) to surrounding vehicles such as the second vehicle. Forexample, the first vehicle may maintain a consistent speed, but IAcomputing device 104 may categorize the speed as a risk if the firstvehicle is traveling 55 mph on a local street where surrounding vehiclessuch as the second vehicle are traveling at 25 mph. In this example, IAcomputing device 104 may determine that the first vehicle exceeds afirst threshold that is based upon the posted speed limit, andsubsequently determine that the first vehicle poses a medium or highrisk to the second vehicle. However, if the first vehicle in thisexample continues to accelerate to 70 mph, IA computing device 104 maydetermine that the first vehicle exceeds a second and/or thirdthreshold, and subsequently determine corrective action accordingly.

IA computing device 104 may further transmit 756 a message that includesthe analyzed sensor data of the first vehicle and/or the first driver toa second vehicle (e.g., target vehicle 204) or other nearby vehicles viawireless communication or data transmission over one or more radiofrequency links. In some embodiments, the message may provideinformation as to the type of risk (e.g., lane departure, weaving in andout of lanes, and speeding) and category of risk (e.g., low, medium, orhigh) presented by the first vehicle to the second vehicle. In furtherembodiments where the second vehicle has autonomous functionalities, themessage may include a recommendation (or control signal) for a vehiclecontroller of the second vehicle, such as target vehicle controller 208(shown in FIG. 2) to engage an automated safety system of the secondvehicle such as an automatic or semi-automatic braking system,acceleration system, and/or steering system. For example, the messagemay indicate that the first vehicle poses a high risk to the secondvehicle (e.g., speeding through a red light at a 4-way intersection),and may recommend that the second vehicle engage an automated steeringsystem to avoid the first vehicle.

A controller or processor at the second vehicle such as target vehiclecontroller 208 (shown in FIG. 2) may analyze 758 the message receivedfrom the first vehicle (including analyzing image, audio, telematics, orother first vehicle sensor data) to determine that the first vehiclepresents a risk to the second vehicle. For instance, the first driver(of the first vehicle) may be determined to be operating the vehicleabnormally or may be distracted, and/or the first vehicle speed,direction, and operation, such as determined from the first vehicletelematics data, may be determined to be abnormal or above a riskthreshold. In one embodiment, telematics data from the first vehicle(e.g., analyzed sensor data) may indicate lane drift or variation inspeed, which may indicate driver drowsiness or distraction. In certainembodiments, the controller or processor of the second vehicle maycompare the analyzed sensor data of the received message to sensor datacollected by sensors mounted on the second vehicle.

Process 750 may further include determining 760 one or more correctiveactions (e.g., collision avoidance actions) for the second vehicle. Insome embodiments, the second vehicle may take action based upon arecommendation (included in the transmitted message) generated by the IAcomputing device 104 in the first vehicle. In other embodiments, thesecond vehicle may generate an alert (e.g., auditory, visual, and/orhaptic) to a second driver of the second vehicle and/or recommend thatone or more safety feature be engaged by the second driver. In furtherembodiments where the second vehicle possesses autonomousfunctionalities, the second vehicle may automatically engage anautomated safety system of the second vehicle such as an automaticbraking system, acceleration system, and/or steering system.

An insurance provider may collect the sensor data, and data associatedwith operation of the first vehicle and/or the second vehicle to adjust762 an insurance policy of the first driver and/or the second driver.For example, an insurance discount may be provided for the second driver(of the second vehicle) based upon the second vehicle being equippedwith the foregoing functionality and other functionality discussedherein. Process 750 may include addition, less, or alternate actions,including those discussed elsewhere herein.

Although as described herein, data is collected for the first vehicle,this is for exemplary purposes only. Data may also be collected by thesecond vehicle and provided to IA computing device 104 of the firstvehicle, enabling IA computing device 104 to determine whether toprovide an alert or take control of the first vehicle.

Exemplary Sensor Embodiment

FIG. 8 depicts an exemplary sensor embodiment 800 on host vehicle (e.g.,first vehicle) 100 (shown in FIG. 1) in accordance with one embodimentof the present disclosure. Sensor embodiment 800 shows one version ofarranging the plurality of sensors 102 (shown in FIG. 1) on host vehicle100 to capture data of target vehicle (e.g., second vehicle) 204.

In the exemplary embodiment, sensors 102 on host vehicle 100 may includea plurality of cameras, such as front camera 802, right camera 804, andleft camera 806. Cameras 802, 804, and 806 are outward-facing camerasthat continuously capture high quality images and/or video necessary foranalysis by IA computing device 104. Cameras 802, 804, and 806 maypossess video recording capabilities, and may record continuously whilethe ignition of host vehicle 100 is powered on. In the exemplaryembodiment, front camera 802 may be positioned on the rearview mirror ofhost vehicle 100. In some embodiments, front camera 802 may bepositioned near the rearview mirror on the dashboard of host vehicle100.

Front camera 802 may be a front view camera capable of capturing data ofboth oncoming vehicles and the drivers of the oncoming vehicles (e.g.,opposing drivers). Right camera 804 and left camera 806 may be side viewcameras capable of capturing vehicle condition data and driver data ofvehicles of parallel traffic. Cameras 804 and 806 may be positioned ator near the side mirrors of host vehicle 100. Similar to front camera802, right camera 804 and left camera 806 may be positioned at an anglethat enables cameras 804 and 806 to capture vehicle and driver data. Insome embodiments, host vehicle 100 may utilize a front view camera, suchas front camera 802, and only one side view camera (right camera 804 orleft camera 806).

In the exemplary embodiment, IA computing device 104 assesses the datafrom cameras 802, 804, and 806 to determine impairment (e.g., operatorimpairment, vehicle impairment). In other embodiments, the data fromcameras 802, 804, and 06 may be analyzed by IA computing device 104 todetermine if target vehicle (e.g., second vehicle) poses a drivinghazard to host vehicle (e.g., first vehicle) 100. Data captured fromcameras 802, 804, and 806 may simultaneously be transmitted to IAcomputing device 104 for filtering and analysis. In other embodiments,data achieving a certain threshold (e.g., minimum image/video qualitynecessary for analysis) may be transmitted to IA computing device 104for analysis.

In other embodiments, additional sensors may be included on host vehicle100. For example, host vehicle 100 may include sensors such as LIDAR,radar, and/or ultrasound in addition to cameras 802, 804, and 806. Insome embodiments, host vehicle 100 may include additional cameraspositioned at different locations on the dashboard and/or on the sidesof host vehicle 100. In other embodiments, host vehicle 100 may includerear view facing cameras positioned in the rear of host vehicle 100 tocapture data of oncoming vehicles approaching from the back.Additionally or alternatively, host vehicle 100 may also use a 3D camera(e.g., depth-discerning/depth camera).

Exemplary Use Case

FIG. 9 illustrates an exemplary use case 900 of the exemplary sensorembodiment 800 shown in FIG. 8 using system 400 (shown in FIG. 4) tocapture sensor data of oncoming traffic. In particular, exemplary usecase 900 illustrates the implementation of front camera 802 to capturedata of target vehicle 204.

In the exemplary embodiment, front camera 802 may be positioned on therearview mirror of host vehicle 100 (as shown in FIG. 8). Front camera802 may be oriented in a position and angle that enables optimal datacapture for analysis by IA computing device 104 (shown in FIG. 1).Cameras 802, 804, and 806 may be configured to detect positional data ofthe target driver. In the exemplary embodiment, front camera 802 is afront view camera that may be configured to capture data of oncomingvehicles.

Front camera 802 may be configured to capture the target driver'spositional data, such as eye movement, head orientation, neck movement,and body posture. Front camera 802 may also be configured to capture atleast one of the target driver's shoulder movement, gaze direction,blinking frequency/duration, head motion (e.g., head nodding, rolling,shaking), arm position, facial expressions, and/or behavioral movements,such as yawning and eating/drinking. In the exemplary embodiment, frontcamera 802 may also be configured to capture the surrounding imagery,and collect information such as the time of day, location, weathercondition, traffic condition, and road condition. Front camera 802 mayalso be configured to capture vehicle speed, lane edges and markings oftarget vehicle 204, and driving behavior such as braking (e.g., hardbraking, aggressive starts). Front camera 802 may also be configured tocapture vehicle condition data of target vehicle 204, and collectinformation pertaining to vehicle make/model, engine, tires, and vehiclemaintenance. In other embodiments, host vehicle 100 may have additionalcameras and/or image capturing sensors accompanying front camera 802 atthe front of host vehicle 100.

Another Exemplary Use Case

FIG. 10 illustrates another exemplary use case 1000 of implementing theexemplary sensor embodiment 800 shown in FIG. 8 using system 400 (shownin FIG. 4) to capture sensor data of parallel traffic. In particular,exemplary use case 1000 illustrates the implementation of right camera804 and left camera 806 of host vehicle 100 to capture data of targetvehicle 204.

Exemplary use case 1000 is a top view of the exemplary sensor embodiment800 shown in FIG. 8. In the exemplary embodiment, right camera 804 maybe located on the passenger side view mirror. Right camera 804 may beconfigured to capture data pertaining to vehicles traveling in the rightlane alongside host vehicle 100. In the exemplary embodiment, leftcamera 806 may be located on the driver side view mirror. Left camera806 may be configured to capture data pertaining to vehicles travelingin the left lane alongside host vehicle 100. Cameras 804 and 806 may bepositioned at different locations on the passenger side and/or driverside of host vehicle 100 for optimal data collection.

Similar to front camera 802, right camera 804 and left camera 806 may beconfigured to capture the target driver's positional data, such as headorientation, neck movement, and body posture. In some embodiments,cameras 804 and 806 may also be configured to detect and capture thetarget driver's eye movement, blinking frequency/duration, and facialexpressions from a side view. In these embodiments, IA computing device104 may be equipped with a baseline model that sets out conditions forside eye movement and facial detection to assess sensor data capturedfrom left camera 806 and right camera 804.

Similar to front camera 802, right camera 804 and left camera 806 mayalso be configured to capture at least one of the target driver'sshoulder movement, gaze direction, blinking frequency/duration, headmotion (e.g., head nodding, rolling, shaking), arm position, and/orbehavioral movements, such as yawning and eating/drinking. Cameras 804and 806 may also be configured to capture the surrounding imagery,vehicle speed, lane edges and markings of target vehicle 204, drivingbehavior such as braking (e.g., hard braking, aggressive starts), andvehicle condition data of target vehicle 204, such as informationpertaining to vehicle make/model, engine, tires, and vehiclemaintenance. In the exemplary embodiment, front camera 802 may beconfigured to capture data of oncoming vehicles in real time as leftcamera 806 and right camera 804 capture data of parallel vehicles to theleft and right of host vehicle 100.

Secondary View of Exemplary Use Case

FIG. 11 illustrates a secondary view 1100 of exemplary use case 1000(shown in FIG. 10). In particular, FIG. 11 illustrates a front view ofhost vehicle 100 and target vehicle 204 as right camera 804 of theexemplary sensor embodiment 800 (shown in FIG. 8) uses system 400 (shownin FIG. 4) to capture sensor data of a parallel vehicle.

In the exemplary embodiment, right camera 804, which is positioned onthe passenger side of host vehicle 100, may be configured to scan thetarget driver of target vehicle 204 to capture positional datapertaining to the target driver. In some embodiments, right camera 804may be configured to scan a wider area of the driver side of targetvehicle 204. For example, right camera 804 may be configured to scan (i)the driver window of target vehicle 204 to acquire positional data ofthe target driver, and (ii) the entire driver side of target vehicle 204(e.g., front tire, driver door, and roof) to acquire vehicle conditiondata.

In the exemplary embodiment, left camera 806, which is positioned on thedriver side of host vehicle 100, may be configured to scan the vehiclestraveling in the left lane alongside host vehicle 100. Similar to rightcamera 804, left camera 806 may be configured to capture positional datapertaining to the target driver in the left lane. In some embodiments,left camera 806 may be equipped with sensing and focusing technologythat enables left camera 806 to identify and recognize the target driverfrom a greater distance. In other embodiments, left camera 806 mayinclude capabilities that enable left camera 806 to recognize the targetdriver in the left lane and acquire data when the passenger seat oftarget vehicle 204 is occupied. In these embodiments, left camera 806may be a 3D (depth-focusing camera). Left camera 806 may also beaccompanied by one or more additional cameras or image-capturing devicesthat enable IA computing device 104 (shown in FIG. 1) to acquirepositional data and vehicle condition data of target vehicle 204traveling in the left lane.

Exemplary Computer Device

FIG. 12 depicts a diagram 1200 of components of one or more exemplarycomputing devices 1220 that may be used in system 400 shown in FIG. 4.In some embodiments, computing device 1220 may be similar to IAcomputing device 104 (shown in FIG. 1). Database 1230 may be coupledwith several separate components within computing device 1220, whichperform specific tasks. In this embodiment, database 1230 may includethe baseline model 1232, which includes the baseline conditions.Database 1230 may also include sensor data 1234. Sensor data 1234 mayinclude vehicle condition data of target vehicle 204, and driver data,such as positional data of the target driver. In some embodiments,database 1230 is similar to database 404 (shown in FIG. 4).

Computing device 1220 may include database 1230, as well as data storagedevices 1202. Computing device 1220 may also include a communicationcomponent 1204 for interrogating 302 target vehicle 204 and receiving304 sensor data (both shown in FIG. 3). In some embodiments,communication component 1204 may be for receiving 702 second vehicledata (shown in FIG. 7). Computing device 1220 may further include ananalyzing component 1206 for analyzing 306 the sensor data (shown inFIG. 3). In some embodiments, analyzing component 1206 may be foranalyzing 704 the second vehicle data (shown in FIG. 7). Moreover,computing device 1220 may include a detecting component 1208 fordetecting 308 an impairment (shown in FIG. 3). In some embodiments,detecting component 1208 may be for determining 706 that a secondvehicle poses a driving hazard to a first vehicle (shown in FIG. 7).

Computing device 1220 may further include an outputting component 1210for outputting 310 an alert signal (shown in FIG. 3). In someembodiments, outputting component 1210 may be for generating 708 analert signal (shown in FIG. 7). A processing component 1212 may assistwith execution of computer-executable instructions associated with thesystem.

Exemplary Embodiments & Functionality

In one aspect, an impairment analysis (“IA”) computer system fordetecting a driver and/or vehicle impairment to facilitate vehiclecollision avoidance may be provided. The IA computer system may beassociated with a host vehicle. The IA computer system may include aplurality of sensors and at least one processor in communication withthe plurality of sensors and at least one memory device. The at leastone processor may be configured or programmed to: (1) interrogate atarget vehicle via a plurality of sensors by scanning at least one ofthe target vehicle and a target driver of the target vehicle; (2)receive sensor data including at least one of target driver data andtarget vehicle condition data; (3) analyze the sensor data bydetermining at least one outlier within the sensor data; (4) detect animpairment of at least one of the target driver and the target vehiclebased upon the analysis; and/or (5) direct corrective action based uponthe impairment of the at least one of the target driver and the targetvehicle.

A further enhancement may be where the at least one outlier deviatesfrom a model stored in the at least one memory device. The model mayrepresent safe driving parameters and standard vehicle maintenanceconditions.

A further enhancement may be where the computer system directscorrective action by generating an alert at the host vehicle. The alertmay be at least one of an auditory alert, a visual alert, and a hapticalert.

A further enhancement may be where the computer system directscorrective action by automatically engaging an automated safety systemof the host vehicle. The automated safety system may be at least one ofan automated braking system and an automated steering system.

A further enhancement may be where the computer system directscorrective action by generating a recommendation at the host vehicle toengage an automated safety system of the host vehicle. Engaging theautomated safety system may reduce a probability of colliding with thetarget vehicle.

A further enhancement may be where the computer system directscorrective action by automatically engaging at least one of anautonomous vehicle control system and a semi-autonomous vehicle controlsystem of the host vehicle.

A further enhancement may be where the computer system directscorrective action by generating a recommendation at the host vehicle toengage at least one of an autonomous vehicle system and asemi-autonomous vehicle system of the host vehicle.

A further enhancement may be where the host vehicle operates in at leastone of a semi-autonomous control mode and an autonomous control mode.The at least one processor may further programmed to direct correctiveaction by instructing a vehicle control system of the host vehicle tosteer the host vehicle away from a path of the target vehicle.

A further enhancement may be where the impairment is based upon adeviation in at least one of speed maintenance and lane maintenance ofthe target vehicle.

In another aspect, an impairment analysis (“IA”) computer system fordetecting an impairment may be provided. The IA computer system may beassociated with a host vehicle. The IA computing system may include atleast one processor in communication with at least one memory device.The at least one processor may be configured or programmed to: (1)interrogate a target vehicle by using a plurality of sensors included ona host vehicle to scan the target vehicle and a target driver; (2)receive sensor data including target driver data and target vehiclecondition data; (3) analyze the sensor data by applying a baseline modelto the sensor data; (4) detect an impairment of the target driver ortarget vehicle based upon the analysis; and/or (5) output an alertsignal to a host vehicle controller, or direct other collision avoidanceactions (such as engage an autonomous or semi-autonomous vehicle systemor other automated safety feature), based upon the determination thatthe target driver or target vehicle is impaired.

A further enhancement may be where the sensor data includes video datafrom a plurality of cameras. The plurality of cameras are configured todetect positional data. The positional data may include at least one ofeye movement, head orientation, neck position, and body posture of thetarget driver of the target vehicle.

A further enhancement may be where the IA computer system stores thebaseline model in the at least one memory device. The baseline model mayinclude baseline conditions representing safe driving conditions andstandard vehicle maintenance conditions.

A further enhancement may be where the alert signal is at least one ofan auditory, a visual signal, and a haptic signal. A further enhancementmay be where the IA computer system outputs the alert signal to at leastone of a target vehicle controller and a vehicle controller of asurrounding vehicle. A further enhancement may be where scanningincludes at least one of repeated visual scanning by the plurality ofsensors, and receiving a wireless communication by the plurality ofsensors.

A further enhancement may be where the target driver data is associatedwith the target driver, and includes information pertaining to at leastone of speed, vehicle lane maintenance (e.g., lane drifting), braking,and posture. The information pertaining to posture (e.g., positionaldata) may include data related to head orientation, body posture, andeye movement. A further enhancement may be where the target vehiclecondition data is associated with the target vehicle, and includesinformation pertaining to at least one of vehicle maintenance, enginecondition, and road condition.

A further enhancement may be where the plurality of sensors includes awireless communications device. Receiving the sensor data by the IAcomputer system may include receiving, at the wireless communicationsdevice, an alert message from the target vehicle.

A further enhancement may be where the IA computer system (i) stores thesensor data in the at least one memory device, and (ii) transmits thesensor data to a remote-computing device to update at least one of anunderwriting model and an actuarial model. The sensor data may be usedto adjust an insurance policy of an insurance holder.

In another aspect, an impairment analysis (“IA”) computer system foralerting a first driver of a first vehicle to a driving hazard posed bya second vehicle may be provided. The IA computer system may beassociated with the first vehicle, and may include at least oneprocessor in communication with at least one memory device. The at leastone processor may be programmed to: (1) receive second vehicle dataincluding second driver data and second vehicle condition data, whereinthe second vehicle data is collected by a plurality of sensors includedon the first vehicle; (2) analyze the second vehicle data by applying abaseline model to the second vehicle data; (3) determine that the secondvehicle poses a driving hazard to the first vehicle based upon theanalysis; and/or (4) generate an alert signal based upon thedetermination that the second vehicle poses a driving hazard to thefirst vehicle.

A further enhancement may be where the second vehicle data includesvideo data from a plurality of cameras. The plurality of cameras may beconfigured to detect positional data. The positional data may include atleast one of eye position, head position, neck position, and bodyposture of the second driver of the second vehicle.

A further enhancement may be where the IA computer system stores thebaseline model in the at least one memory device. The baseline model mayinclude baseline conditions representing safe driving conditions andstandard vehicle maintenance conditions.

A further enhancement may be where the driver data is associated withthe second driver, and may include information pertaining to at leastone of speed, vehicle lane maintenance (e.g., lane drifting), braking,and posture. The information pertaining to posture (e.g., positionaldata) may include data related to head position, body position, and eyeposition. A further enhancement may be where the vehicle condition datais associated with the second vehicle, and includes informationpertaining to at least one of vehicle maintenance, engine condition, androad condition.

A further enhancement may be where the alert signal is at least one ofan auditory signal, a visual signal, and a haptic signal. A furtherenhancement may be where the IA computer system further outputs thealert signal to at least one of a second vehicle controller and avehicle controller of a surrounding vehicle.

A further enhancement may be where scanning includes at least one ofrepeated visual scanning by the plurality of sensors, and receiving awireless communication by the plurality of sensors. A furtherenhancement may be where the plurality of sensors includes a wirelesscommunications device. Receiving the second vehicle data by the IAcomputer system may include receiving, at the wireless communicationsdevice, an alert message from the second vehicle.

A further enhancement may be where the IA computer system (i) stores thesecond vehicle data in the at least one memory device, and (ii)transmits the second vehicle data to a remote-computing device to updateat least one of an underwriting model and an actuarial model. The secondvehicle data may be used to adjust an insurance policy of an insuranceholder.

In another aspect, an impairment analysis (“IA”) computer system fordetecting a driver or vehicle impairment may be provided. The IAcomputer system may be associated with a host vehicle, and may include aplurality of sensors. The IA computer system may include at least oneprocessor in communication with the plurality of sensors and at leastone memory device. The at least one processor may be programmed to: (1)interrogate a target vehicle, wherein the target vehicle operates in atleast one of a semi-autonomous control mode and an autonomous controlmode, and wherein the target vehicle may communicate in real-time withthe host vehicle; (2) receive, from the target vehicle, sensor dataincluding target driver data and target vehicle condition data; (3)analyze the sensor data by applying a model stored in the IA computersystem to the sensor data; (4) detect an impairment of the target driveror target vehicle based upon the analysis; and/or (5) output an alertsignal to a host vehicle controller based upon detecting that the targetdriver or target vehicle is impaired, or otherwise exhibiting abnormaldriving behavior or operating abnormally, respectively.

In yet another aspect, a computer system for collecting real-timeimpaired driving data may be provided. The computer system may include aplurality of sensors. The computer system may further include at leastone processor in communication with the plurality of sensors and atleast one memory device. The at least one processor may be programmedto: (1) interrogate a target vehicle via the plurality of sensors byscanning the target vehicle and/or a target driver of the targetvehicle; (2) receive sensor data including target driver data and/ortarget vehicle condition data; (3) analyze the sensor data by applying abaseline model to the sensor data; (4) detect an impairment of thetarget driver and/or target vehicle based upon the analysis, wherein theimpairment is one of at least a high-risk impairment and a low-riskimpairment; and/or (5) transmit the detected impairment to aremote-computing device to update an insurance policy of an insuranceholder.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on vehicles ormobile devices, or associated with smart infrastructure or remoteservers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, a reinforced or reinforcement learningmodule or program, or a combined learning module or program that learnsin two or more fields or areas of interest. Machine learning may involveidentifying and recognizing patterns in existing data in order tofacilitate making predictions for subsequent data. Models may be createdbased upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as images, object statistics and information, historical estimates,and/or actual repair costs. The machine learning programs may utilizedeep learning algorithms that may be primarily focused on patternrecognition, and may be trained after processing multiple examples. Themachine learning programs may include Bayesian Program Learning (BPL),voice recognition and synthesis, image or object recognition, opticalcharacter recognition, and/or natural language processing—eitherindividually or in combination. The machine learning programs may alsoinclude natural language processing, semantic analysis, automaticreasoning, and/or machine learning.

Supervised and unsupervised machine learning techniques may be used. Insupervised machine learning, a processing element may be provided withexample inputs and their associated outputs, and may seek to discover ageneral rule that maps inputs to outputs, so that when subsequent novelinputs are provided the processing element may, based upon thediscovered rule, accurately predict the correct output. In unsupervisedmachine learning, the processing element may be required to find its ownstructure in unlabeled example inputs. In one embodiment, machinelearning techniques may be used to extract data about the object,vehicle, user, damage, needed repairs, costs and/or incident fromvehicle data, insurance policies, geolocation data, image data, and/orother data.

Based upon these analyses, the processing element may learn how toidentify characteristics and patterns that may then be applied toanalyzing image data, model data, and/or other data. For example, theprocessing element may learn, with the user's permission or affirmativeconsent, to identify the type of incident that occurred based uponimages of the resulting damage. The processing element may also learnhow to identify damage that may not be readily visible based upon thereceived image data.

Additional Considerations

As will be appreciated based upon the foregoing specification, theabove-described embodiments of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code means, may beembodied or provided within one or more computer-readable media, therebymaking a computer program product, i.e., an article of manufacture,according to the discussed embodiments of the disclosure. Thecomputer-readable media may be, for example, but is not limited to, afixed (hard) drive, diskette, optical disk, magnetic tape, semiconductormemory such as read-only memory (ROM), and/or any transmitting/receivingmedium, such as the Internet or other communication network or link. Thearticle of manufacture containing the computer code may be made and/orused by executing the code directly from one medium, by copying the codefrom one medium to another medium, or by transmitting the code over anetwork.

These computer programs (also known as programs, software, softwareapplications, “apps”, or code) include machine instructions for aprogrammable processor, and can be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refers to any computer programproduct, apparatus and/or device (e.g., magnetic discs, optical disks,memory, Programmable Logic Devices (PLDs)) used to provide machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions as amachine-readable signal. The “machine-readable medium” and“computer-readable medium,” however, do not include transitory signals.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an exemplary embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality.

In some embodiments, the system includes multiple components distributedamong a plurality of computing devices. One or more components may be inthe form of computer-executable instructions embodied in acomputer-readable medium. The systems and processes are not limited tothe specific embodiments described herein. In addition, components ofeach system and each process can be practiced independent and separatefrom other components and processes described herein. Each component andprocess can also be used in combination with other assembly packages andprocesses. The present embodiments may enhance the functionality andfunctioning of computers and/or computer systems.

As used herein, an element or step recited in the singular and precededby the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

The patent claims at the end of this document are not intended to beconstrued under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being expressly recited in the claim(s).

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the disclosure, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal languages of the claims.

We claim:
 1. An impairment analysis (“IA”) computer system for detectingvehicular impairment to facilitate vehicle collision avoidance, the IAcomputer system associated with a host vehicle, the IA computer systemcomprising a plurality of sensors, and at least one processor incommunication with the plurality of sensors and at least one memorydevice, the at least one processor is programmed to: interrogate atarget vehicle via the plurality of sensors by scanning the targetvehicle; receive sensor data including target vehicle condition data andtarget vehicle operation data, the target vehicle condition dataincluding data representing a current engine condition of the targetvehicle, target vehicle operation data including data representing acurrent operation of the target vehicle; analyze the sensor data byapplying the sensor data to a baseline model representing standardvehicle operation and maintenance conditions for the target vehicle,wherein the baseline model accounts for a current location of the targetvehicle, current driving conditions experienced by the target vehicle,and a current time when the sensor data was captured, and wherein thebaseline model identifies a level of vehicular impairment indicating alikelihood that a vehicle collision will occur between the targetvehicle and the host vehicle; detect a vehicular impairment of thetarget vehicle based upon the analysis by identifying at least one dataoutlier within the sensor data that indicates the vehicle impairment;identify the level of vehicular impairment from the analysis of thesensor data by the baseline model; transmit the detected vehicularimpairment and the level of vehicular impairment of the target vehicleto a remote-computing device to update an insurance policy of aninsurance holder; and direct corrective action based upon the vehicularimpairment and the level of vehicular impairment of the target vehicle.2. The system of claim 1, wherein the at least one processor is furtherprogrammed to direct corrective action by generating an alert at thehost vehicle, and wherein the alert is at least one of an auditoryalert, a visual alert, and a haptic alert.
 3. The system of claim 1,wherein the at least one processor is further programmed to directcorrective action by automatically engaging an automated safety systemof the host vehicle, and wherein the automated safety system is at leastone of an automated braking system and an automated steering system. 4.The system of claim 1, wherein the at least one processor is furtherprogrammed to direct corrective action by generating a recommendation atthe host vehicle to engage an automated safety system of the hostvehicle, and wherein engaging the automated safety system reduces aprobability of colliding with the target vehicle.
 5. The system of claim1, wherein the at least one processor is further programmed to directcorrective action by automatically engaging at least one of anautonomous vehicle control system and a semi-autonomous vehicle controlsystem of the host vehicle.
 6. The system of claim 1, wherein the atleast one processor is further programmed to direct corrective action bygenerating a recommendation at the host vehicle to engage at least oneof an autonomous vehicle system and a semi-autonomous vehicle system ofthe host vehicle.
 7. The system of claim 1, wherein the host vehicleoperates in at least one of a semi-autonomous control mode and anautonomous control mode, and wherein the at least one processor isfurther programmed to direct corrective action by instructing a vehiclecontrol system of the host vehicle to steer the host vehicle away from apath of the target vehicle.
 8. The system of claim 1, wherein thevehicular impairment is based upon a deviation in at least one of speedmaintenance and lane maintenance of the target vehicle and wherein eachlevel of vehicular impairment is associated with at least one differentcorrective action.
 9. The system of claim 1, wherein the at least oneprocessor is further programmed to: interrogate a target driver of thetarget vehicle receive sensor data including target driver data of thetarget driver of the target vehicle; analyze the sensor data by applyingthe sensor data to a baseline model representing safe drivingparameters, wherein the safe driving parameters include at least aplurality of facial features and upper body positions associated withsafe driving postures of a driver; detect an impairment of the targetdriver based upon the analysis; and direct corrective action based uponthe impairment of the target driver.
 10. A computer-implemented methodfor detecting vehicular impairment, the method implemented using animpairment analysis (“IA”) computing device associated with a hostvehicle, the IA computing device including at least one processor incommunication with at least one memory device, the method comprising:interrogating, by the IA computing device, a target vehicle by using aplurality of sensors included on a host vehicle to scan a targetvehicle; receiving, by the IA computing device, sensor data andincluding target vehicle condition data and target vehicle operationdata, the target vehicle condition data including data representing acurrent engine condition of the target vehicle, target vehicle operationdata including data representing a current operation of the targetvehicle; analyzing, by the IA computing device, by applying the sensordata to a baseline model representing standard vehicle operation andmaintenance conditions for the target vehicle, wherein the baselinemodel accounts for a current location of the target vehicle, currentdriving conditions experienced by the target vehicle, and a current timewhen the sensor data was captured, and wherein the baseline modelidentifies a level of vehicular impairment indicating a likelihood thata vehicle collision will occur between the target vehicle and the hostvehicle; detecting, by the IA computing device, a vehicular impairmentof the target vehicle based upon the analysis by identifying at leastone data outlier within the sensor data that indicates the vehicleimpairment; identifying the level of vehicular impairment from theanalysis of the sensor data by the baseline model; transmitting thedetected vehicular impairment and the level of vehicular impairment ofthe target vehicle to a remote-computing device to update an insurancepolicy of an insurance holder; and directing, by the IA computingdevice, corrective action based upon the impairment and the level ofvehicular impairment of the target vehicle.
 11. The computer-implementedmethod of claim 10, wherein analyzing, by the IA computing device,comprises comparing the sensor data to the model to determine the atleast one outlier.
 12. The computer-implemented method of claim 10,wherein directing, by the IA computing device, corrective action basedupon the impairment comprises generating an alert at the host vehicle,and wherein the alert is at least one of an auditory alert, a visualalert, and a haptic alert.
 13. The computer-implemented method of claim10, wherein directing, by the IA computing device, corrective actioncomprises automatically engaging an automated safety system of the hostvehicle, the automated safety system including at least one of anautomated braking system and an automated steering system.
 14. Thecomputer-implemented method of claim 10, wherein directing, by the IAcomputing device, corrective action comprises generating arecommendation at the host vehicle to engage an automated safety systemof the host vehicle, and wherein engaging the automated safety systemreduces a probability of colliding with the target vehicle.
 15. Thecomputer-implemented method of claim 10, wherein directing, by the IAcomputing device, corrective action comprises automatically engaging atleast one of an autonomous vehicle control system and a semi-autonomousvehicle control system.
 16. The computer-implemented method of claim 10,wherein directing, by the IA computing device, a corrective actioncomprises generating a recommendation at the host vehicle to engage atleast one of an autonomous vehicle control system and a semi-autonomousvehicle control system of the host vehicle.
 17. The computer-implementedmethod of claim 10, wherein the host vehicle operates in at least one ofan autonomous control mode and a semi-autonomous control mode, andwherein directing, by the IA computing device, corrective actioncomprises instructing a vehicle control system of the host vehicle tosteer the host vehicle away from a path of the target vehicle.
 18. Thecomputer-implemented method of claim 10, wherein the impairment is basedupon a variation in at least one of speed and lane maintenance of thetarget vehicle.
 19. The computer-implemented method of claim 10, whereinanalyzing, by the IA computing device, by determining at least oneoutlier within the sensor data comprises determining whether a variationin at least one of speed and lane maintenance of the target vehicleexceeds a respective threshold and wherein each level of vehicularimpairment is associated with at least one different corrective action.20. The computer-implemented method of claim 10 further comprising:interrogating a target driver of the target vehicle; receiving sensordata including target driver data of the target driver of the targetvehicle; analyzing the sensor data by applying the sensor data to abaseline model representing safe driving parameters, wherein the safedriving parameters include at least a plurality of facial features andupper body positions associated with safe driving postures of a driver;detecting an impairment of the target driver based upon the analysis;and directing corrective action based upon the impairment of the targetdriver.